一阶段高筛制作完成

.gitignore

@@ -0,0 +1,21 @@
+# --- Python 通用忽略 ---
+__pycache__/
+*.pyc
+*.pyo
+*.pyd
+.Python
+env/
+venv/
+.env
+.venv/
+
+# --- VS Code 配置 (可选，建议忽略) ---
+.vscode/
+
+# --- JetBrains (你之前的配置) ---
+.idea/
+/shelf/
+/workspace.xml
+/httpRequests/
+/dataSources/
+/dataSources.local.xml

.idea/.gitignore

@@ -1,8 +0,0 @@
-# 默认忽略的文件
-/shelf/
-/workspace.xml
-# 基于编辑器的 HTTP 客户端请求
-/httpRequests/
-# Datasource local storage ignored files
-/dataSources/
-/dataSources.local.xml

.idea/Screen.iml

@@ -1,8 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<module type="PYTHON_MODULE" version="4">
-  <component name="NewModuleRootManager">
-    <content url="file://$MODULE_DIR$" />
-    <orderEntry type="jdk" jdkName="Python 3.11" jdkType="Python SDK" />
-    <orderEntry type="sourceFolder" forTests="false" />
-  </component>
-</module>

.idea/inspectionProfiles/Project_Default.xml

@@ -1,23 +0,0 @@
-<component name="InspectionProjectProfileManager">
-  <profile version="1.0">
-    <option name="myName" value="Project Default" />
-    <inspection_tool class="Eslint" enabled="true" level="WARNING" enabled_by_default="true" />
-    <inspection_tool class="PyPackageRequirementsInspection" enabled="true" level="WARNING" enabled_by_default="true">
-      <option name="ignoredPackages">
-        <value>
-          <list size="9">
-            <item index="0" class="java.lang.String" itemvalue="torchvision" />
-            <item index="1" class="java.lang.String" itemvalue="torch" />
-            <item index="2" class="java.lang.String" itemvalue="tqdm" />
-            <item index="3" class="java.lang.String" itemvalue="scipy" />
-            <item index="4" class="java.lang.String" itemvalue="h5py" />
-            <item index="5" class="java.lang.String" itemvalue="matplotlib" />
-            <item index="6" class="java.lang.String" itemvalue="numpy" />
-            <item index="7" class="java.lang.String" itemvalue="opencv_python" />
-            <item index="8" class="java.lang.String" itemvalue="Pillow" />
-          </list>
-        </value>
-      </option>
-    </inspection_tool>
-  </profile>
-</component>

.idea/inspectionProfiles/profiles_settings.xml

@@ -1,6 +0,0 @@
-<component name="InspectionProjectProfileManager">
-  <settings>
-    <option name="USE_PROJECT_PROFILE" value="false" />
-    <version value="1.0" />
-  </settings>
-</component>

.idea/modules.xml

@@ -1,8 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="ProjectModuleManager">
-    <modules>
-      <module fileurl="file://$PROJECT_DIR$/.idea/Screen.iml" filepath="$PROJECT_DIR$/.idea/Screen.iml" />
-    </modules>
-  </component>
-</project>

.idea/vcs.xml

@@ -1,6 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="VcsDirectoryMappings">
-    <mapping directory="$PROJECT_DIR$" vcs="Git" />
-  </component>
-</project>

main.py

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+"""
+高通量筛选与扩胞项目 - 主入口（支持断点续做）
+"""
+import os
+import sys
+import json
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'src'))
+
+from src.analysis.database_analyzer import DatabaseAnalyzer
+from src.analysis.report_generator import ReportGenerator
+from src.core.executor import TaskExecutor
+from src.preprocessing.processor import StructureProcessor
+from src.computation.workspace_manager import WorkspaceManager
+from src.computation.zeo_executor import ZeoExecutor, ZeoConfig
+from src.computation.result_processor import ResultProcessor, FilterCriteria
+
+
+def print_banner():
+    print("""
+╔═══════════════════════════════════════════════════════════════════╗
+║          高通量筛选与扩胞项目 - 数据库分析工具 v2.2              ║
+║                   支持断点续做与高性能并行计算                    ║
+╚═══════════════════════════════════════════════════════════════════╝
+    """)
+
+
+def detect_and_show_environment():
+    """检测并显示环境信息"""
+    env = TaskExecutor.detect_environment()
+
+    print("【运行环境检测】")
+    print(f"  主机名:       {env['hostname']}")
+    print(f"  本地CPU核数:  {env['total_cores']}")
+    print(f"  SLURM集群:    {'✅ 可用' if env['has_slurm'] else '❌ 不可用'}")
+    
+    if env['has_slurm'] and env['slurm_partitions']:
+        print(f"  可用分区:     {', '.join(env['slurm_partitions'])}")
+    
+    if env['conda_env']:
+        print(f"  当前Conda:    {env['conda_env']}")
+
+    return env
+
+
+def detect_workflow_status(workspace_path: str = "workspace", target_cation: str = "Li") -> dict:
+    """
+    检测工作流程状态，确定可以从哪一步继续
+    
+    Returns:
+        dict: {
+            'has_processed_data': bool,  # 是否有扩胞处理后的数据
+            'has_zeo_results': bool,      # 是否有 Zeo++ 计算结果
+            'total_structures': int,      # 总结构数
+            'structures_with_log': int,   # 有 log.txt 的结构数
+            'workspace_info': object,     # 工作区信息
+            'ws_manager': object          # 工作区管理器
+        }
+    """
+    status = {
+        'has_processed_data': False,
+        'has_zeo_results': False,
+        'total_structures': 0,
+        'structures_with_log': 0,
+        'workspace_info': None,
+        'ws_manager': None
+    }
+    
+    # 创建工作区管理器
+    ws_manager = WorkspaceManager(
+        workspace_path=workspace_path,
+        tool_dir="tool",
+        target_cation=target_cation
+    )
+    status['ws_manager'] = ws_manager
+    
+    # 检查是否有处理后的数据
+    data_dir = os.path.join(workspace_path, "data")
+    if os.path.exists(data_dir):
+        existing = ws_manager.check_existing_workspace()
+        if existing and existing.total_structures > 0:
+            status['has_processed_data'] = True
+            status['total_structures'] = existing.total_structures
+            status['workspace_info'] = existing
+            
+            # 检查有多少结构有 log.txt（即已完成 Zeo++ 计算）
+            log_count = 0
+            for anion_key in os.listdir(data_dir):
+                anion_dir = os.path.join(data_dir, anion_key)
+                if not os.path.isdir(anion_dir):
+                    continue
+                for struct_name in os.listdir(anion_dir):
+                    struct_dir = os.path.join(anion_dir, struct_name)
+                    if os.path.isdir(struct_dir):
+                        log_path = os.path.join(struct_dir, "log.txt")
+                        if os.path.exists(log_path):
+                            log_count += 1
+            
+            status['structures_with_log'] = log_count
+            
+            # 如果大部分结构都有 log.txt，认为 Zeo++ 计算已完成
+            if log_count > 0 and log_count >= status['total_structures'] * 0.5:
+                status['has_zeo_results'] = True
+    
+    return status
+
+
+def print_workflow_status(status: dict):
+    """打印工作流程状态"""
+    print("\n" + "─" * 50)
+    print("【工作流程状态检测】")
+    print("─" * 50)
+    
+    if not status['has_processed_data']:
+        print("  Step 1 (扩胞+化合价): ❌ 未完成")
+        print("  Step 2-4 (Zeo++ 计算): ❌ 未完成")
+        print("  Step 5 (结果筛选):     ❌ 未完成")
+    else:
+        print(f"  Step 1 (扩胞+化合价): ✅ 已完成 ({status['total_structures']} 个结构)")
+        
+        if status['has_zeo_results']:
+            print(f"  Step 2-4 (Zeo++ 计算): ✅ 已完成 ({status['structures_with_log']}/{status['total_structures']} 有日志)")
+            print("  Step 5 (结果筛选):     ⏳ 可执行")
+        else:
+            print(f"  Step 2-4 (Zeo++ 计算): ⏳ 可执行 ({status['structures_with_log']}/{status['total_structures']} 有日志)")
+            print("  Step 5 (结果筛选):     ❌ 需先完成 Zeo++ 计算")
+    
+    print("─" * 50)
+
+
+def get_user_choice(status: dict) -> str:
+    """
+    根据工作流程状态获取用户选择
+    
+    Returns:
+        'step1': 从头开始（数据库分析 + 扩胞）
+        'step2': 从 Zeo++ 计算开始
+        'step5': 直接进行结果筛选
+        'exit': 退出
+    """
+    print("\n请选择操作:")
+    
+    options = []
+    
+    if status['has_zeo_results']:
+        options.append(('5', '直接进行结果筛选 (Step 5)'))
+        options.append(('2', '重新运行 Zeo++ 计算 (Step 2-4)'))
+        options.append(('1', '从头开始 (数据库分析 + 扩胞)'))
+    elif status['has_processed_data']:
+        options.append(('2', '运行 Zeo++ 计算 (Step 2-4)'))
+        options.append(('1', '从头开始 (数据库分析 + 扩胞)'))
+    else:
+        options.append(('1', '从头开始 (数据库分析 + 扩胞)'))
+    
+    options.append(('q', '退出'))
+    
+    for key, desc in options:
+        print(f"  {key}. {desc}")
+    
+    choice = input("\n请选择 [默认: " + options[0][0] + "]: ").strip().lower()
+    
+    if not choice:
+        choice = options[0][0]
+    
+    if choice == 'q':
+        return 'exit'
+    elif choice == '5':
+        return 'step5'
+    elif choice == '2':
+        return 'step2'
+    else:
+        return 'step1'
+
+
+def run_step1_database_analysis(env: dict, cation: str) -> dict:
+    """
+    Step 1: 数据库分析与扩胞处理
+    
+    Returns:
+        处理参数字典，如果用户取消则返回 None
+    """
+    print("\n" + "═" * 60)
+    print("【Step 1: 数据库分析与扩胞处理】")
+    print("═" * 60)
+    
+    # 数据库路径
+    while True:
+        db_path = input("\n📂 请输入数据库路径: ").strip()
+        if os.path.exists(db_path):
+            break
+        print(f"❌ 路径不存在: {db_path}")
+
+    # 检测当前Conda环境路径
+    default_conda = "/cluster/home/koko125/anaconda3/envs/screen"
+    conda_env_path = env.get('conda_env', '') or default_conda
+
+    print(f"\n检测到Conda环境: {conda_env_path}")
+    custom_env = input(f"使用此环境? [Y/n] 或输入其他路径: ").strip()
+    if custom_env.lower() == 'n':
+        conda_env_path = input("请输入Conda环境完整路径: ").strip()
+    elif custom_env and custom_env.lower() != 'y':
+        conda_env_path = custom_env
+
+    # 目标阴离子
+    anion_input = input("🎯 请输入目标阴离子 (逗号分隔) [默认: O,S,Cl,Br]: ").strip()
+    anions = set(a.strip() for a in anion_input.split(',')) if anion_input else {'O', 'S', 'Cl', 'Br'}
+
+    # 阴离子模式
+    print("\n阴离子模式:")
+    print("  1. 仅单一阴离子")
+    print("  2. 仅复合阴离子")
+    print("  3. 全部 (默认)")
+    mode_choice = input("请选择 [1/2/3]: ").strip()
+    anion_mode = {'1': 'single', '2': 'mixed', '3': 'all', '': 'all'}.get(mode_choice, 'all')
+
+    # 并行配置
+    print("\n" + "─" * 50)
+    print("【并行计算配置】")
+
+    default_cores = min(env['total_cores'], 32)
+    cores_input = input(f"💻 最大可用核数/Worker数 [默认: {default_cores}]: ").strip()
+    max_workers = int(cores_input) if cores_input.isdigit() else default_cores
+
+    params = {
+        'database_path': db_path,
+        'target_cation': cation,
+        'target_anions': anions,
+        'anion_mode': anion_mode,
+        'max_workers': max_workers,
+        'conda_env': conda_env_path,
+    }
+
+    print("\n" + "═" * 60)
+    print("开始数据库分析...")
+    print("═" * 60)
+
+    # 创建分析器
+    analyzer = DatabaseAnalyzer(
+        database_path=params['database_path'],
+        target_cation=params['target_cation'],
+        target_anions=params['target_anions'],
+        anion_mode=params['anion_mode'],
+        max_cores=params['max_workers'],
+        task_complexity='medium'
+    )
+
+    print(f"\n发现 {len(analyzer.cif_files)} 个CIF文件")
+
+    # 执行分析
+    report = analyzer.analyze(show_progress=True)
+
+    # 打印报告
+    ReportGenerator.print_report(report, detailed=True)
+
+    # 保存选项
+    save_choice = input("\n是否保存报告? [y/N]: ").strip().lower()
+    if save_choice == 'y':
+        output_path = input("报告路径 [默认: analysis_report.json]: ").strip()
+        output_path = output_path or "analysis_report.json"
+        report.save(output_path)
+        print(f"✅ 报告已保存到: {output_path}")
+
+    # CSV导出
+    csv_choice = input("是否导出详细CSV? [y/N]: ").strip().lower()
+    if csv_choice == 'y':
+        csv_path = input("CSV路径 [默认: analysis_details.csv]: ").strip()
+        csv_path = csv_path or "analysis_details.csv"
+        ReportGenerator.export_to_csv(report, csv_path)
+
+    # 生成最终数据库
+    process_choice = input("\n是否生成最终可用的数据库（扩胞+添加化合价）? [Y/n]: ").strip().lower()
+    if process_choice == 'n':
+        print("\n已跳过扩胞处理，可稍后继续")
+        return params
+    
+    # 输出目录设置
+    print("\n输出目录设置:")
+    flat_dir = input("  原始格式输出目录 [默认: workspace/processed]: ").strip()
+    flat_dir = flat_dir or "workspace/processed"
+    
+    analysis_dir = input("  分析格式输出目录 [默认: workspace/data]: ").strip()
+    analysis_dir = analysis_dir or "workspace/data"
+    
+    # 扩胞保存数量
+    keep_input = input("\n扩胞结构保存数量 [默认: 1]: ").strip()
+    keep_number = int(keep_input) if keep_input.isdigit() and int(keep_input) > 0 else 1
+    
+    # 扩胞精度选择
+    print("\n扩胞计算精度:")
+    print("  1. 高精度 (精确分数)")
+    print("  2. 普通精度 (分母≤100)")
+    print("  3. 低精度 (分母≤10) [默认]")
+    print("  4. 极低精度 (分母≤5)")
+    precision_choice = input("请选择 [1/2/3/4]: ").strip()
+    calculate_type = {
+        '1': 'high', '2': 'normal', '3': 'low', '4': 'very_low', '': 'low'
+    }.get(precision_choice, 'low')
+    
+    # 获取可处理文件
+    processable = report.get_processable_files(include_needs_expansion=True)
+    
+    if not processable:
+        print("⚠️ 没有可处理的文件")
+        return params
+    
+    print(f"\n发现 {len(processable)} 个可处理的文件")
+    
+    # 准备文件列表和扩胞标记
+    input_files = [info.file_path for info in processable]
+    needs_expansion_flags = [info.needs_expansion for info in processable]
+    anion_types_list = [info.anion_types for info in processable]
+    
+    direct_count = sum(1 for f in needs_expansion_flags if not f)
+    expansion_count = sum(1 for f in needs_expansion_flags if f)
+    print(f"  - 可直接处理: {direct_count}")
+    print(f"  - 需要扩胞:   {expansion_count}")
+    print(f"  - 扩胞保存数: {keep_number}")
+    
+    confirm = input("\n确认开始处理? [Y/n]: ").strip().lower()
+    if confirm == 'n':
+        print("\n已取消处理")
+        return params
+    
+    print("\n" + "═" * 60)
+    print("开始处理结构文件...")
+    print("═" * 60)
+    
+    # 创建处理器
+    processor = StructureProcessor(
+        calculate_type=calculate_type,
+        keep_number=keep_number,
+        target_cation=params['target_cation']
+    )
+    
+    # 创建输出目录
+    os.makedirs(flat_dir, exist_ok=True)
+    os.makedirs(analysis_dir, exist_ok=True)
+    
+    results = []
+    total = len(input_files)
+    
+    import shutil
+    for i, (input_file, needs_exp, anion_types) in enumerate(
+        zip(input_files, needs_expansion_flags, anion_types_list)
+    ):
+        print(f"\r处理进度: {i+1}/{total} - {os.path.basename(input_file)}", end="")
+        
+        # 处理文件到原始格式目录
+        result = processor.process_file(input_file, flat_dir, needs_exp)
+        results.append(result)
+        
+        if result.success:
+            # 同时保存到分析格式目录
+            # 按阴离子类型创建子目录
+            anion_key = '+'.join(sorted(anion_types)) if anion_types else 'other'
+            anion_dir = os.path.join(analysis_dir, anion_key)
+            os.makedirs(anion_dir, exist_ok=True)
+            
+            # 获取基础文件名
+            base_name = os.path.splitext(os.path.basename(input_file))[0]
+            
+            # 创建以文件名命名的子目录
+            file_dir = os.path.join(anion_dir, base_name)
+            os.makedirs(file_dir, exist_ok=True)
+            
+            # 复制生成的文件到分析格式目录
+            for output_file in result.output_files:
+                dst_path = os.path.join(file_dir, os.path.basename(output_file))
+                shutil.copy2(output_file, dst_path)
+    
+    print()
+    
+    # 统计结果
+    success_count = sum(1 for r in results if r.success)
+    fail_count = sum(1 for r in results if not r.success)
+    total_output = sum(len(r.output_files) for r in results if r.success)
+    
+    print("\n" + "-" * 60)
+    print("【处理结果统计】")
+    print("-" * 60)
+    print(f"  成功处理: {success_count}")
+    print(f"  处理失败: {fail_count}")
+    print(f"  生成文件: {total_output}")
+    print(f"\n  原始格式目录: {flat_dir}")
+    print(f"  分析格式目录: {analysis_dir}")
+    print(f"    └── 结构: data/阴离子类型/文件名/文件名.cif")
+    
+    # 显示失败的文件
+    if fail_count > 0:
+        print("\n失败的文件:")
+        for r in results:
+            if not r.success:
+                print(f"  - {os.path.basename(r.input_file)}: {r.error_message}")
+    
+    print("\n✅ Step 1 完成!")
+    return params
+
+
+def run_step2_zeo_analysis(params: dict, ws_manager: WorkspaceManager = None, workspace_info = None):
+    """
+    Step 2-4: Zeo++ Voronoi 分析
+    
+    Args:
+        params: 参数字典，包含 target_cation 等
+        ws_manager: 工作区管理器（可选，如果已有则直接使用）
+        workspace_info: 工作区信息（可选，如果已有则直接使用）
+    """
+    
+    print("\n" + "═" * 60)
+    print("【Step 2-4: Zeo++ Voronoi 分析】")
+    print("═" * 60)
+    
+    # 如果没有传入 ws_manager，则创建新的
+    if ws_manager is None:
+        # 工作区路径
+        workspace_path = input("\n工作区路径 [默认: workspace]: ").strip() or "workspace"
+        tool_dir = input("工具目录路径 [默认: tool]: ").strip() or "tool"
+        
+        # 创建工作区管理器
+        ws_manager = WorkspaceManager(
+            workspace_path=workspace_path,
+            tool_dir=tool_dir,
+            target_cation=params.get('target_cation', 'Li')
+        )
+    
+    # 如果没有传入 workspace_info，则检查现有工作区
+    if workspace_info is None:
+        existing = ws_manager.check_existing_workspace()
+        
+        if existing and existing.total_structures > 0:
+            ws_manager.print_workspace_summary(existing)
+            workspace_info = existing
+        else:
+            print("⚠️ 工作区数据目录不存在或为空")
+            print(f"   请先运行 Step 1 生成处理后的数据到: {ws_manager.data_dir}")
+            return
+    
+    # 检查是否需要创建软链接
+    if workspace_info.linked_structures < workspace_info.total_structures:
+        print("\n正在创建配置文件软链接...")
+        workspace_info = ws_manager.setup_workspace(force_relink=False)
+    
+    # 获取计算任务
+    tasks = ws_manager.get_computation_tasks(workspace_info)
+    
+    if not tasks:
+        print("⚠️ 没有找到可计算的任务")
+        return
+    
+    print(f"\n发现 {len(tasks)} 个计算任务")
+    
+    # Zeo++ 环境配置
+    print("\n" + "-" * 50)
+    print("【Zeo++ 计算配置】")
+    
+    default_zeo_env = "/cluster/home/koko125/anaconda3/envs/zeo"
+    zeo_env = input(f"Zeo++ Conda环境 [默认: {default_zeo_env}]: ").strip()
+    zeo_env = zeo_env or default_zeo_env
+    
+    # SLURM 配置
+    partition = input("SLURM分区 [默认: cpu]: ").strip() or "cpu"
+    
+    max_concurrent_input = input("最大并发任务数 [默认: 50]: ").strip()
+    max_concurrent = int(max_concurrent_input) if max_concurrent_input.isdigit() else 50
+    
+    time_limit = input("单任务时间限制 [默认: 2:00:00]: ").strip() or "2:00:00"
+    
+    # 创建配置
+    zeo_config = ZeoConfig(
+        conda_env=zeo_env,
+        partition=partition,
+        max_concurrent=max_concurrent,
+        time_limit=time_limit
+    )
+    
+    # 确认执行
+    print("\n" + "-" * 50)
+    print("【计算任务确认】")
+    print(f"  总任务数:     {len(tasks)}")
+    print(f"  Conda环境:    {zeo_config.conda_env}")
+    print(f"  SLURM分区:    {zeo_config.partition}")
+    print(f"  最大并发:     {zeo_config.max_concurrent}")
+    print(f"  时间限制:     {zeo_config.time_limit}")
+    
+    confirm = input("\n确认提交计算任务? [Y/n]: ").strip().lower()
+    if confirm == 'n':
+        print("已取消")
+        return
+    
+    # 创建执行器并运行
+    executor = ZeoExecutor(zeo_config)
+    
+    log_dir = os.path.join(ws_manager.workspace_path, "slurm_logs")
+    results = executor.run_batch(tasks, output_dir=log_dir)
+    
+    # 打印结果摘要
+    executor.print_results_summary(results)
+    
+    # 保存结果
+    results_file = os.path.join(ws_manager.workspace_path, "zeo_results.json")
+    results_data = [
+        {
+            'task_id': r.task_id,
+            'structure_name': r.structure_name,
+            'cif_path': r.cif_path,
+            'success': r.success,
+            'output_files': r.output_files,
+            'error_message': r.error_message
+        }
+        for r in results
+    ]
+    with open(results_file, 'w') as f:
+        json.dump(results_data, f, indent=2)
+    print(f"\n结果已保存到: {results_file}")
+    
+    print("\n✅ Step 2-4 完成!")
+
+
+def run_step5_result_processing(workspace_path: str = "workspace"):
+    """
+    Step 5: 结果处理与筛选
+    
+    Args:
+        workspace_path: 工作区路径
+    """
+    print("\n" + "═" * 60)
+    print("【Step 5: 结果处理与筛选】")
+    print("═" * 60)
+    
+    # 创建结果处理器
+    processor = ResultProcessor(workspace_path=workspace_path)
+    
+    # 获取筛选条件
+    print("\n设置筛选条件:")
+    print("  (直接回车使用默认值，输入 0 表示不限制)")
+    
+    # 最小渗透直径（默认改为 1.0）
+    perc_input = input("  最小渗透直径 (Å) [默认: 1.0]: ").strip()
+    min_percolation = float(perc_input) if perc_input else 1.0
+    
+    # 最小 d 值
+    d_input = input("  最小 d 值 [默认: 2.0]: ").strip()
+    min_d = float(d_input) if d_input else 2.0
+    
+    # 最大节点长度
+    node_input = input("  最大节点长度 (Å) [默认: 不限制]: ").strip()
+    max_node = float(node_input) if node_input else float('inf')
+    
+    # 创建筛选条件
+    criteria = FilterCriteria(
+        min_percolation_diameter=min_percolation,
+        min_d_value=min_d,
+        max_node_length=max_node
+    )
+    
+    # 执行处理
+    results, stats = processor.process_and_filter(
+        criteria=criteria,
+        save_csv=True,
+        copy_passed=True
+    )
+    
+    # 打印摘要
+    processor.print_summary(results, stats)
+    
+    # 显示输出位置
+    print("\n输出文件位置:")
+    print(f"  汇总 CSV:     {workspace_path}/results/summary.csv")
+    print(f"  分类 CSV:     {workspace_path}/results/阴离子类型/阴离子类型.csv")
+    print(f"  通过筛选:     {workspace_path}/passed/阴离子类型/结构名/")
+    
+    print("\n✅ Step 5 完成!")
+
+
+def main():
+    """主函数"""
+    print_banner()
+
+    # 环境检测
+    env = detect_and_show_environment()
+    
+    # 询问目标阳离子
+    cation = input("\n🎯 请输入目标阳离子 [默认: Li]: ").strip() or "Li"
+    
+    # 检测工作流程状态
+    status = detect_workflow_status(workspace_path="workspace", target_cation=cation)
+    print_workflow_status(status)
+    
+    # 获取用户选择
+    choice = get_user_choice(status)
+    
+    if choice == 'exit':
+        print("\n👋 再见!")
+        return
+    
+    # 根据选择执行相应步骤
+    params = {'target_cation': cation}
+    
+    if choice == 'step1':
+        # 从头开始
+        params = run_step1_database_analysis(env, cation)
+        if params is None:
+            return
+        
+        # 询问是否继续
+        continue_choice = input("\n是否继续进行 Zeo++ 计算? [Y/n]: ").strip().lower()
+        if continue_choice == 'n':
+            print("\n可稍后运行程序继续 Zeo++ 计算")
+            return
+        
+        # 重新检测状态
+        status = detect_workflow_status(workspace_path="workspace", target_cation=cation)
+        run_step2_zeo_analysis(params, status['ws_manager'], status['workspace_info'])
+        
+        # 询问是否继续筛选
+        continue_choice = input("\n是否继续进行结果筛选? [Y/n]: ").strip().lower()
+        if continue_choice == 'n':
+            print("\n可稍后运行程序继续结果筛选")
+            return
+        
+        run_step5_result_processing("workspace")
+        
+    elif choice == 'step2':
+        # 从 Zeo++ 计算开始
+        run_step2_zeo_analysis(params, status['ws_manager'], status['workspace_info'])
+        
+        # 询问是否继续筛选
+        continue_choice = input("\n是否继续进行结果筛选? [Y/n]: ").strip().lower()
+        if continue_choice == 'n':
+            print("\n可稍后运行程序继续结果筛选")
+            return
+        
+        run_step5_result_processing("workspace")
+        
+    elif choice == 'step5':
+        # 直接进行结果筛选
+        run_step5_result_processing("workspace")
+    
+    print("\n✅ 全部完成!")
+
+
+if __name__ == "__main__":
+    main()

readme.md

@@ -1,4 +1,4 @@
-# 高通量筛选与扩胞项目
+# 高通量筛选与扩胞项目 v2.3
 
 ## 环境配置需求
 
@@ -12,9 +12,29 @@
 ### 2. screen 环境 (用于逻辑筛选与数据处理)
 *   **Python**: 3.11.4
 *   **核心库**: `pymatgen==2024.11.13`, `pandas` (新增，用于处理CSV)
+*   **路径**: `/cluster/home/koko125/anaconda3/envs/screen`
 
 ## 快速开始
 
+### 方式一：使用新版主程序（推荐）
+
+```bash
+# 激活 screen 环境
+conda activate /cluster/home/koko125/anaconda3/envs/screen
+
+# 运行主程序
+python main.py
+```
+
+主程序提供交互式界面，支持：
+- 数据库分析与筛选
+- 本地多进程并行
+- SLURM 直接提交（无需生成脚本文件）
+- 实时进度条显示
+- 扩胞处理与化合价添加
+
+### 方式二：传统方式
+
 1.  **数据准备**:
     *   如果数据来源为 **Materials Project (MP)**，请将 CIF 文件放入 `data/input_pre`。
     *   如果数据来源为 **ICSD**，请直接将 CIF 文件放入 `data/input`。
@@ -25,6 +45,52 @@
         bash main.sh
         ```
 
+## 新版功能特性 (v2.1)
+
+### 执行模式
+
+1. **本地多进程模式** (`local`)
+   - 使用 Python multiprocessing 在本地并行执行
+   - 适合小规模任务或测试
+
+2. **SLURM 直接提交模式** (`slurm`)
+   - 直接在 Python 中提交 SLURM 作业
+   - 无需生成脚本文件
+   - 实时监控作业状态和进度
+   - 适合大规模高通量计算
+
+### 进度显示
+
+```
+分析CIF文件: |████████████████████░░░░░░░░░░░░░░░░░░░░| 500/1000 (50.0%) [0:05:23<0:05:20, 1.6it/s] ✓480 ✗20
+```
+
+### 输出格式
+
+处理后的文件支持两种输出格式：
+
+1. **原始格式**（平铺）
+   ```
+   workspace/processed/
+   ├── 1514027.cif
+   ├── 1514072.cif
+   └── ...
+   ```
+
+2. **分析格式**（按阴离子分类）
+   ```
+   workspace/data/
+   ├── O/
+   │   ├── 1514027/
+   │   │   └── 1514027.cif
+   │   └── 1514072/
+   │       └── 1514072.cif
+   ├── S/
+   │   └── ...
+   └── Cl+O/
+       └── ...
+   ```
+
 ## 处理流程详解
 
 ### Stage 1: 预处理与基础筛选 (Step 1)
@@ -51,9 +117,12 @@
 
 ---
 
-## 扩胞逻辑 (Step 5 - 待后续执行)
+## 扩胞逻辑 (Step 5)
 
-目前扩胞逻辑维持原状，基于筛选后的结构进行处理。
+扩胞处理已集成到新版主程序中，支持：
+- 自动计算扩胞因子
+- 可选保存数量（当为1时不加后缀）
+- 自动添加化合价信息
 
 ### 算法分解
 1.  **读取结构**: 解析 CIF 文件。
@@ -71,4 +140,276 @@
 
 ### 假设条件
 *   只考虑两个原子在同一位置上的共占位情况。
-*   不考虑 Li 原子的共占位情况，对 Li 原子不做处理。
+*   不考虑 Li 原子的共占位情况，对 Li 原子不做处理。
+
+## 项目结构
+
+```
+screen/
+├── main.py                 # 主入口（新版）
+├── main.sh                 # 传统脚本入口
+├── readme.md               # 本文档
+├── config/                 # 配置文件
+│   ├── settings.yaml
+│   └── valence_states.yaml
+├── src/                    # 源代码
+│   ├── analysis/           # 分析模块
+│   │   ├── database_analyzer.py
+│   │   ├── report_generator.py
+│   │   ├── structure_inspector.py
+│   │   └── worker.py
+│   ├── core/               # 核心模块
+│   │   ├── executor.py     # 任务执行器（新）
+│   │   ├── scheduler.py    # 调度器
+│   │   └── progress.py     # 进度管理
+│   ├── preprocessing/      # 预处理模块
+│   │   ├── processor.py    # 结构处理器
+│   │   └── ...
+│   └── utils/              # 工具函数
+├── py/                     # 传统脚本
+├── tool/                   # 工具和配置
+│   ├── analyze_voronoi_nodes.py
+│   └── Li/                 # 化合价配置
+└── workspace/              # 工作区
+    ├── data/               # 分析格式输出
+    └── processed/          # 原始格式输出
+```
+
+## API 使用示例
+
+### 使用执行器
+
+```python
+from src.core.executor import create_executor, TaskExecutor
+
+# 创建执行器
+executor = create_executor(
+    mode="slurm",  # 或 "local"
+    max_workers=32,
+    conda_env="/cluster/home/koko125/anaconda3/envs/screen"
+)
+
+# 定义任务
+tasks = [
+    (file_path, "Li", {"O", "S"})
+    for file_path in cif_files
+]
+
+# 执行
+from src.analysis.worker import analyze_single_file
+results = executor.run(tasks, analyze_single_file, desc="分析CIF文件")
+```
+
+### 使用数据库分析器
+
+```python
+from src.analysis.database_analyzer import DatabaseAnalyzer
+
+analyzer = DatabaseAnalyzer(
+    database_path="/path/to/cif/files",
+    target_cation="Li",
+    target_anions={"O", "S", "Cl", "Br"},
+    anion_mode="all"
+)
+
+report = analyzer.analyze(show_progress=True)
+report.save("analysis_report.json")
+```
+
+### 使用 Zeo++ 执行器
+
+```python
+from src.computation.workspace_manager import WorkspaceManager
+from src.computation.zeo_executor import ZeoExecutor, ZeoConfig
+
+# 设置工作区
+ws_manager = WorkspaceManager(
+    workspace_path="workspace",
+    tool_dir="tool",
+    target_cation="Li"
+)
+
+# 创建软链接
+workspace_info = ws_manager.setup_workspace()
+
+# 获取计算任务
+tasks = ws_manager.get_computation_tasks(workspace_info)
+
+# 配置 Zeo++ 执行器
+config = ZeoConfig(
+    conda_env="/cluster/home/koko125/anaconda3/envs/zeo",
+    partition="cpu",
+    max_concurrent=50,
+    time_limit="2:00:00"
+)
+
+# 执行计算
+executor = ZeoExecutor(config)
+results = executor.run_batch(tasks, output_dir="slurm_logs")
+executor.print_results_summary(results)
+```
+
+## 新版功能特性 (v2.2)
+
+### Zeo++ Voronoi 分析
+
+新增 SLURM 作业数组支持，可高效调度大量 Zeo++ 计算任务：
+
+1. **自动工作区管理**
+   - 检测现有工作区数据
+   - 自动创建配置文件软链接
+   - 按阴离子类型组织目录结构
+
+2. **SLURM 作业数组**
+   - 使用 `--array` 参数批量提交任务
+   - 支持最大并发数限制（如 `%50`）
+   - 自动分批处理超大任务集
+
+3. **实时进度监控**
+   - 通过状态文件跟踪任务完成情况
+   - 支持 Ctrl+C 中断监控（作业继续运行）
+   - 自动收集输出文件
+
+### 工作流程
+
+```
+Step 1: 数据库分析
+    ↓
+Step 1.5: 扩胞处理 + 化合价添加
+    ↓
+Step 2-4: Zeo++ Voronoi 分析
+    ├── 创建软链接 (yaml 配置 + 计算脚本)
+    ├── 提交 SLURM 作业数组
+    └── 监控进度并收集结果
+    ↓
+Step 5: 结果处理与筛选
+    ├── 从 log.txt 提取关键参数
+    ├── 汇总到 CSV 文件
+    ├── 应用筛选条件
+    └── 复制通过筛选的结构到 passed/ 目录
+```
+
+### 筛选条件
+
+Step 5 支持以下筛选条件：
+- **最小渗透直径** (Percolation Diameter): 默认 1.0 Å
+- **最小 d 值** (Minimum of d): 默认 2.0
+- **最大节点长度** (Maximum Node Length): 默认不限制
+
+### 日志输出
+
+Zeo++ 计算的输出会重定向到每个结构目录下的 `log.txt` 文件：
+```bash
+python analyze_voronoi_nodes.py *.cif -i O.yaml > log.txt 2>&1
+```
+
+日志中包含的关键信息：
+- `Percolation diameter (A): X.XX` - 渗透直径
+- `the minium of d\nX.XX` - 最小 d 值
+- `Maximum node length detected: X.XX A` - 最大节点长度
+
+### 目录结构
+
+```
+workspace/
+├── data/                   # 分析格式数据
+│   ├── O/                  # 氧化物
+│   │   ├── O.yaml -> tool/Li/O.yaml
+│   │   ├── analyze_voronoi_nodes.py -> tool/analyze_voronoi_nodes.py
+│   │   ├── 1514027/
+│   │   │   ├── 1514027.cif
+│   │   │   ├── 1514027_all_accessed_node.cif  # Zeo++ 输出
+│   │   │   ├── 1514027_bond_valence_filtered.cif
+│   │   │   └── 1514027_bv_info.csv
+│   │   └── ...
+│   ├── S/                  # 硫化物
+│   └── Cl+O/               # 复合阴离子
+├── processed/              # 原始格式数据
+├── slurm_logs/             # SLURM 日志
+│   ├── tasks.json
+│   ├── submit_array.sh
+│   ├── task_0.out
+│   ├── task_0.err
+│   ├── status_0.txt
+│   └── ...
+└── zeo_results.json        # 计算结果汇总
+```
+
+### 配置文件说明
+
+`tool/Li/O.yaml` 示例：
+```yaml
+SPECIE: Li+
+ANION: O
+PERCO_R: 0.5
+NEIGHBOR: 1.8
+LONG: 2.2
+```
+
+参数说明：
+- `SPECIE`: 目标扩散离子（带电荷）
+- `ANION`: 阴离子类型
+- `PERCO_R`: 渗透半径阈值
+- `NEIGHBOR`: 邻近距离阈值
+- `LONG`: 长节点判定阈值
+
+## 新版功能特性 (v2.3)
+
+### 断点续做功能
+
+v2.3 新增智能断点续做功能，支持从任意步骤继续执行：
+
+1. **自动状态检测**
+   - 启动时自动检测工作流程状态
+   - 检测 `workspace/data/` 是否存在且有结构 → 判断 Step 1 是否完成
+   - 检测结构目录下是否有 `log.txt` → 判断 Zeo++ 计算是否完成
+   - 如果 50% 以上结构有 log.txt，认为 Zeo++ 计算已完成
+
+2. **智能流程跳转**
+   - 如果已完成 Zeo++ 计算 → 可直接进行筛选
+   - 如果已完成扩胞处理 → 可直接进行 Zeo++ 计算
+   - 从后往前检测，自动跳过已完成的步骤
+
+3. **分步执行与中断**
+   - 每个大步骤完成后询问是否继续
+   - 支持中途退出，下次运行时自动检测进度
+   - 三大步骤：扩胞与加化合价 → Zeo++ 计算 → 筛选
+
+### 工作流程状态示例
+
+```
+         工作流程状态检测
+
+检测到现有工作区数据:
+  - 结构总数: 1234
+  - 已完成 Zeo++ 计算: 1200 (97.2%)
+  - 未完成 Zeo++ 计算: 34
+
+当前状态: Zeo++ 计算已完成
+
+可选操作:
+  [1] 直接进行结果筛选 (Step 5)
+  [2] 重新运行 Zeo++ 计算 (Step 2-4)
+  [3] 从头开始 (Step 1)
+  [0] 退出
+
+请选择 [1]:
+```
+
+### 使用场景
+
+1. **首次运行**
+   - 从 Step 1 开始完整执行
+   - 每步完成后可选择继续或退出
+
+2. **中断后继续**
+   - 自动检测已完成的步骤
+   - 提供从当前进度继续的选项
+
+3. **重新筛选**
+   - 修改筛选条件后
+   - 可直接运行 Step 5 而无需重新计算
+
+4. **部分重算**
+   - 如需重新计算部分结构
+   - 可选择重新运行 Zeo++ 计算

src/__init__.py

@@ -0,0 +1,12 @@
+"""
+高通量筛选与扩胞项目 - 源代码包
+"""
+
+from . import analysis
+from . import core
+from . import preprocessing
+from . import computation
+from . import utils
+
+__version__ = "2.2.0"
+__all__ = ['analysis', 'core', 'preprocessing', 'computation', 'utils']

src/analysis/database_analyzer.py

@@ -0,0 +1,468 @@
+"""
+数据库分析器：支持高性能并行分析
+"""
+import os
+import pickle
+import json
+from dataclasses import dataclass, field, asdict
+from typing import Dict, List, Set, Optional
+from pathlib import Path
+
+from .structure_inspector import StructureInspector, StructureInfo
+from .worker import analyze_single_file
+from ..core.scheduler import ParallelScheduler, ResourceConfig
+
+
+# 在 DatabaseReport 类中添加缺失的字段
+
+@dataclass
+class DatabaseReport:
+    """数据库分析报告"""
+
+    # 基础统计
+    database_path: str = ""
+    total_files: int = 0
+    valid_files: int = 0
+    invalid_files: int = 0
+
+    # 目标元素统计
+    target_cation: str = ""
+    target_anions: Set[str] = field(default_factory=set)
+    anion_mode: str = ""
+
+    # 含目标阳离子的统计
+    cation_containing_count: int = 0
+    cation_containing_ratio: float = 0.0
+
+    # 阴离子分布
+    anion_distribution: Dict[str, int] = field(default_factory=dict)
+    anion_ratios: Dict[str, float] = field(default_factory=dict)
+    single_anion_count: int = 0
+    mixed_anion_count: int = 0
+
+    # 数据质量统计
+    with_oxidation_states: int = 0
+    without_oxidation_states: int = 0
+    needs_expansion_count: int = 0
+    cation_with_vacancy_count: int = 0  # Li与空位共占位（新增）
+    cation_with_other_cation_count: int = 0  # Li与其他阳离子共占位（新增）
+    anion_partial_occupancy_count: int = 0
+    binary_compound_count: int = 0
+    has_water_count: int = 0
+    has_radioactive_count: int = 0
+
+    # 可处理性统计
+    directly_processable: int = 0
+    needs_preprocessing: int = 0
+    cannot_process: int = 0
+
+    # 详细信息
+    all_structures: List[StructureInfo] = field(default_factory=list)
+    skip_reasons_summary: Dict[str, int] = field(default_factory=dict)
+
+    # 扩胞相关统计（新增）
+    expansion_stats: Dict[str, int] = field(default_factory=lambda: {
+        'no_expansion_needed': 0,
+        'expansion_factor_2': 0,
+        'expansion_factor_3': 0,
+        'expansion_factor_4_8': 0,
+        'expansion_factor_large': 0,
+        'cannot_expand': 0,
+    })
+    expansion_factor_distribution: Dict[int, int] = field(default_factory=dict)
+
+    def to_dict(self) -> dict:
+        """转换为可序列化的字典"""
+        from dataclasses import fields as dataclass_fields
+
+        def convert_value(val):
+            """递归转换值为可序列化类型"""
+            if isinstance(val, set):
+                return list(val)
+            elif isinstance(val, dict):
+                return {k: convert_value(v) for k, v in val.items()}
+            elif isinstance(val, list):
+                return [convert_value(item) for item in val]
+            elif hasattr(val, '__dataclass_fields__'):
+                # 处理 dataclass 对象
+                return {k: convert_value(v) for k, v in asdict(val).items()}
+            else:
+                return val
+
+        result = {}
+        for f in dataclass_fields(self):
+            value = getattr(self, f.name)
+            result[f.name] = convert_value(value)
+
+        return result
+
+    def save(self, path: str):
+        """保存报告到JSON文件"""
+        with open(path, 'w', encoding='utf-8') as f:
+            json.dump(self.to_dict(), f, indent=2, ensure_ascii=False)
+        print(f"✅ 报告已保存到: {path}")
+
+    @classmethod
+    def load(cls, path: str) -> 'DatabaseReport':
+        """从JSON文件加载报告"""
+        with open(path, 'r', encoding='utf-8') as f:
+            d = json.load(f)
+
+        # 处理 set 类型
+        if 'target_anions' in d:
+            d['target_anions'] = set(d['target_anions'])
+        # 处理 StructureInfo 列表（简化处理，不恢复完整对象）
+        if 'all_structures' in d:
+            d['all_structures'] = []
+
+        return cls(**d)
+
+    def get_processable_files(self, include_needs_expansion: bool = True) -> List[StructureInfo]:
+        """
+        获取可处理的文件列表
+        
+        Args:
+            include_needs_expansion: 是否包含需要扩胞的文件
+            
+        Returns:
+            可处理的 StructureInfo 列表
+        """
+        result = []
+        for info in self.all_structures:
+            if info is None or not info.is_valid:
+                continue
+            if not info.contains_target_cation:
+                continue
+            if not info.can_process:
+                continue
+            if not include_needs_expansion and info.needs_expansion:
+                continue
+            result.append(info)
+        return result
+
+    def copy_processable_files(
+        self, 
+        output_dir: str, 
+        include_needs_expansion: bool = True,
+        organize_by_anion: bool = True
+    ) -> Dict[str, int]:
+        """
+        将可处理的CIF文件复制到工作区
+        
+        Args:
+            output_dir: 输出目录（如 workspace/data）
+            include_needs_expansion: 是否包含需要扩胞的文件
+            organize_by_anion: 是否按阴离子类型组织子目录
+            
+        Returns:
+            复制统计信息 {类别: 数量}
+        """
+        import shutil
+        
+        # 创建输出目录
+        os.makedirs(output_dir, exist_ok=True)
+        
+        # 获取可处理文件
+        processable = self.get_processable_files(include_needs_expansion)
+        
+        stats = {
+            'direct': 0,           # 可直接处理
+            'needs_expansion': 0,  # 需要扩胞
+            'total': 0
+        }
+        
+        # 按类型创建子目录
+        if organize_by_anion:
+            anion_dirs = {}
+        
+        for info in processable:
+            # 确定目标目录
+            if organize_by_anion and info.anion_types:
+                # 使用主要阴离子作为目录名
+                anion_key = '+'.join(sorted(info.anion_types))
+                if anion_key not in anion_dirs:
+                    anion_dir = os.path.join(output_dir, anion_key)
+                    os.makedirs(anion_dir, exist_ok=True)
+                    anion_dirs[anion_key] = anion_dir
+                target_dir = anion_dirs[anion_key]
+            else:
+                target_dir = output_dir
+            
+            # 进一步按处理类型分类
+            if info.needs_expansion:
+                sub_dir = os.path.join(target_dir, 'needs_expansion')
+                stats['needs_expansion'] += 1
+            else:
+                sub_dir = os.path.join(target_dir, 'direct')
+                stats['direct'] += 1
+            
+            os.makedirs(sub_dir, exist_ok=True)
+            
+            # 复制文件
+            src_path = info.file_path
+            dst_path = os.path.join(sub_dir, info.file_name)
+            
+            try:
+                shutil.copy2(src_path, dst_path)
+                stats['total'] += 1
+            except Exception as e:
+                print(f"⚠️ 复制失败 {info.file_name}: {e}")
+        
+        # 打印统计
+        print(f"\n📁 文件已复制到: {output_dir}")
+        print(f"   可直接处理: {stats['direct']}")
+        print(f"   需要扩胞:   {stats['needs_expansion']}")
+        print(f"   总计:       {stats['total']}")
+        
+        return stats
+
+
+class DatabaseAnalyzer:
+    """数据库分析器 - 支持高性能并行"""
+
+    def __init__(
+        self,
+        database_path: str,
+        target_cation: str = "Li",
+        target_anions: Set[str] = None,
+        anion_mode: str = "all",
+        max_cores: int = 4,
+        task_complexity: str = "medium"
+    ):
+        """
+        初始化分析器
+
+        Args:
+            database_path: 数据库路径
+            target_cation: 目标阳离子
+            target_anions: 目标阴离子集合
+            anion_mode: 阴离子模式
+            max_cores: 最大可用核数
+            task_complexity: 任务复杂度 ('low', 'medium', 'high')
+        """
+        self.database_path = database_path
+        self.target_cation = target_cation
+        self.target_anions = target_anions or {'O', 'S', 'Cl', 'Br'}
+        self.anion_mode = anion_mode
+        self.max_cores = max_cores
+        self.task_complexity = task_complexity
+
+        # 获取文件列表
+        self.cif_files = self._get_cif_files()
+
+        # 配置调度器
+        self.resource_config = ParallelScheduler.recommend_config(
+            num_tasks=len(self.cif_files),
+            task_complexity=task_complexity,
+            max_cores=max_cores
+        )
+        self.scheduler = ParallelScheduler(self.resource_config)
+
+    def _get_cif_files(self) -> List[str]:
+        """获取所有CIF文件路径"""
+        cif_files = []
+
+        if os.path.isfile(self.database_path):
+            if self.database_path.endswith('.cif'):
+                cif_files.append(self.database_path)
+        else:
+            for root, dirs, files in os.walk(self.database_path):
+                for f in files:
+                    if f.endswith('.cif'):
+                        cif_files.append(os.path.join(root, f))
+
+        return sorted(cif_files)
+
+    def analyze(self, show_progress: bool = True) -> DatabaseReport:
+        """
+        执行并行分析
+
+        Args:
+            show_progress: 是否显示进度
+
+        Returns:
+            DatabaseReport: 分析报告
+        """
+        report = DatabaseReport(
+            database_path=self.database_path,
+            target_cation=self.target_cation,
+            target_anions=self.target_anions,
+            anion_mode=self.anion_mode,
+            total_files=len(self.cif_files)
+        )
+
+        if report.total_files == 0:
+            print(f"⚠️ 警告: 在 {self.database_path} 中未找到CIF文件")
+            return report
+
+        # 准备任务
+        tasks = [
+            (f, self.target_cation, self.target_anions)
+            for f in self.cif_files
+        ]
+
+        # 执行并行分析
+        results = self.scheduler.run_local(
+            tasks=tasks,
+            worker_func=analyze_single_file,
+            desc="分析CIF文件"
+        )
+
+        # 过滤有效结果
+        report.all_structures = [r for r in results if r is not None]
+
+        # 统计结果
+        self._compute_statistics(report)
+
+        return report
+
+    def analyze_slurm(
+        self,
+        output_dir: str,
+        job_name: str = "cif_analysis"
+    ) -> str:
+        """
+        提交SLURM作业进行分析
+
+        Args:
+            output_dir: 输出目录
+            job_name: 作业名称
+
+        Returns:
+            作业ID
+        """
+        os.makedirs(output_dir, exist_ok=True)
+
+        # 保存任务配置
+        tasks_file = os.path.join(output_dir, "tasks.json")
+        with open(tasks_file, 'w') as f:
+            json.dump({
+                'files': self.cif_files,
+                'target_cation': self.target_cation,
+                'target_anions': list(self.target_anions),
+                'anion_mode': self.anion_mode
+            }, f)
+
+        # 生成SLURM脚本
+        worker_script = os.path.join(
+            os.path.dirname(__file__), 'worker.py'
+        )
+        script = self.scheduler.generate_slurm_script(
+            tasks_file=tasks_file,
+            worker_script=worker_script,
+            output_dir=output_dir,
+            job_name=job_name
+        )
+
+        # 保存并提交
+        script_path = os.path.join(output_dir, "submit.sh")
+        return self.scheduler.submit_slurm_job(script, script_path)
+
+
+    # 更新 _compute_statistics 方法
+
+    def _compute_statistics(self, report: DatabaseReport):
+        """计算统计数据"""
+
+        for info in report.all_structures:
+            # 确保 info 不是 None
+            if info is None:
+                report.invalid_files += 1
+                continue
+
+            # 检查有效性
+            if info.is_valid:
+                report.valid_files += 1
+            else:
+                report.invalid_files += 1
+                continue  # 无效文件不继续统计
+
+            # 关键修复：只有当结构确实含有目标阳离子时才计入统计
+            if not info.contains_target_cation:
+                continue  # 不含目标阳离子的文件不继续统计
+
+            report.cation_containing_count += 1
+
+            for anion in info.anion_types:
+                report.anion_distribution[anion] = \
+                    report.anion_distribution.get(anion, 0) + 1
+
+            if info.anion_mode == "single":
+                report.single_anion_count += 1
+            elif info.anion_mode == "mixed":
+                report.mixed_anion_count += 1
+
+            # 根据阴离子模式过滤
+            if self.anion_mode == "single" and info.anion_mode != "single":
+                continue
+            if self.anion_mode == "mixed" and info.anion_mode != "mixed":
+                continue
+            if info.anion_mode == "none":
+                continue
+
+            # 各项统计
+            if info.has_oxidation_states:
+                report.with_oxidation_states += 1
+            else:
+                report.without_oxidation_states += 1
+
+            # Li共占位统计（修改）
+            if info.cation_with_vacancy:
+                report.cation_with_vacancy_count += 1
+            if info.cation_with_other_cation:
+                report.cation_with_other_cation_count += 1
+
+            if info.anion_has_partial_occupancy:
+                report.anion_partial_occupancy_count += 1
+            if info.is_binary_compound:
+                report.binary_compound_count += 1
+            if info.has_water_molecule:
+                report.has_water_count += 1
+            if info.has_radioactive_elements:
+                report.has_radioactive_count += 1
+
+            # 可处理性
+            if info.can_process:
+                if info.needs_expansion:
+                    report.needs_preprocessing += 1
+                else:
+                    report.directly_processable += 1
+            else:
+                report.cannot_process += 1
+                if info.skip_reason:
+                    for reason in info.skip_reason.split("; "):
+                        report.skip_reasons_summary[reason] = \
+                            report.skip_reasons_summary.get(reason, 0) + 1
+
+            # 扩胞统计（新增）
+            exp_info = info.expansion_info
+            factor = exp_info.expansion_factor
+
+            if not exp_info.needs_expansion:
+                report.expansion_stats['no_expansion_needed'] += 1
+            elif not exp_info.can_expand:
+                report.expansion_stats['cannot_expand'] += 1
+            elif factor == 2:
+                report.expansion_stats['expansion_factor_2'] += 1
+            elif factor == 3:
+                report.expansion_stats['expansion_factor_3'] += 1
+            elif 4 <= factor <= 8:
+                report.expansion_stats['expansion_factor_4_8'] += 1
+            else:
+                report.expansion_stats['expansion_factor_large'] += 1
+
+            # 详细分布
+            if exp_info.needs_expansion and exp_info.can_expand:
+                report.expansion_factor_distribution[factor] = \
+                    report.expansion_factor_distribution.get(factor, 0) + 1
+                report.needs_expansion_count += 1
+
+        # 计算比例
+        if report.valid_files > 0:
+            report.cation_containing_ratio = \
+                report.cation_containing_count / report.valid_files
+
+        if report.cation_containing_count > 0:
+            for anion, count in report.anion_distribution.items():
+                report.anion_ratios[anion] = \
+                    count / report.cation_containing_count

src/analysis/report_generator.py

@@ -0,0 +1,159 @@
+"""
+报告生成器：生成格式化的分析报告
+"""
+from typing import Optional
+from .database_analyzer import DatabaseReport
+
+
+class ReportGenerator:
+    """报告生成器"""
+
+    @staticmethod
+    def print_report(report: DatabaseReport, detailed: bool = False):
+        """打印分析报告"""
+
+        print("\n" + "=" * 70)
+        print("                     数据库分析报告")
+        print("=" * 70)
+
+        # 基础信息
+        print(f"\n📁 数据库路径: {report.database_path}")
+        print(f"🎯 目标阳离子: {report.target_cation}")
+        print(f"🎯 目标阴离子: {', '.join(sorted(report.target_anions))}")
+        print(f"🎯 阴离子模式: {report.anion_mode}")
+
+        # 基础统计
+        print("\n" + "-" * 70)
+        print("【1. 基础统计】")
+        print("-" * 70)
+        print(f"  总 CIF 文件数:        {report.total_files}")
+        print(f"  有效文件数:           {report.valid_files}")
+        print(f"  无效文件数:           {report.invalid_files}")
+        print(f"  含 {report.target_cation} 化合物数:       {report.cation_containing_count}")
+        print(f"  含 {report.target_cation} 化合物占比:     {report.cation_containing_ratio:.1%}")
+
+        # 阴离子分布
+        print("\n" + "-" * 70)
+        print(f"【2. 阴离子分布】(在含 {report.target_cation} 的化合物中)")
+        print("-" * 70)
+
+        if report.anion_distribution:
+            for anion in sorted(report.anion_distribution.keys()):
+                count = report.anion_distribution[anion]
+                ratio = report.anion_ratios.get(anion, 0)
+                bar = "█" * int(ratio * 30)
+                print(f"  {anion:5s}: {count:6d} ({ratio:6.1%}) {bar}")
+
+        print(f"\n  单一阴离子化合物:     {report.single_anion_count}")
+        print(f"  复合阴离子化合物:     {report.mixed_anion_count}")
+
+        # 数据质量
+        print("\n" + "-" * 70)
+        print("【3. 数据质量检查】")
+        print("-" * 70)
+
+        total_target = report.cation_containing_count
+        if total_target > 0:
+            print(f"  含化合价信息:         {report.with_oxidation_states:6d} "
+                  f"({report.with_oxidation_states / total_target:.1%})")
+            print(f"  缺化合价信息:         {report.without_oxidation_states:6d} "
+                  f"({report.without_oxidation_states / total_target:.1%})")
+            print()
+            print(f"  {report.target_cation}与空位共占位(无需处理): {report.cation_with_vacancy_count:6d}")
+            print(f"  {report.target_cation}与阳离子共占位(需扩胞): {report.cation_with_other_cation_count:6d}")
+            print(f"  阴离子共占位:         {report.anion_partial_occupancy_count:6d}")
+            print(f"  需扩胞处理(总计):     {report.needs_expansion_count:6d}")
+            print()
+            print(f"  二元化合物:           {report.binary_compound_count:6d}")
+            print(f"  含水分子:             {report.has_water_count:6d}")
+            print(f"  含放射性元素:         {report.has_radioactive_count:6d}")
+
+        # 可处理性评估
+        print("\n" + "-" * 70)
+        print("【4. 可处理性评估】")
+        print("-" * 70)
+
+        total_processable = report.directly_processable + report.needs_preprocessing
+        print(f"  ✅ 可直接处理:        {report.directly_processable:6d}")
+        print(f"  ⚠️  需预处理(扩胞):    {report.needs_preprocessing:6d}")
+        print(f"  ❌ 无法处理:          {report.cannot_process:6d}")
+        print(f"  ─────────────────────────────")
+        print(f"  📊 可处理总数:        {total_processable:6d}")
+
+        # 跳过原因汇总
+        if report.skip_reasons_summary:
+            print("\n" + "-" * 70)
+            print("【5. 无法处理的原因统计】")
+            print("-" * 70)
+            sorted_reasons = sorted(
+                report.skip_reasons_summary.items(),
+                key=lambda x: x[1],
+                reverse=True
+            )
+            for reason, count in sorted_reasons:
+                print(f"  {reason:35s}: {count:6d}")
+
+        # 扩胞分析
+        print("\n" + "-" * 70)
+        print("【6. 扩胞需求分析】")
+        print("-" * 70)
+
+        exp = report.expansion_stats
+        if total_processable > 0:
+            print(f"  无需扩胞:             {exp['no_expansion_needed']:6d}")
+            print(f"  扩胞因子=2:           {exp['expansion_factor_2']:6d}")
+            print(f"  扩胞因子=3:           {exp['expansion_factor_3']:6d}")
+            print(f"  扩胞因子=4~8:         {exp['expansion_factor_4_8']:6d}")
+            print(f"  扩胞因子>8:           {exp['expansion_factor_large']:6d}")
+            print(f"  无法扩胞(因子过大):   {exp['cannot_expand']:6d}")
+
+        # 详细分布
+        if detailed and report.expansion_factor_distribution:
+            print("\n  扩胞因子分布:")
+            for factor in sorted(report.expansion_factor_distribution.keys()):
+                count = report.expansion_factor_distribution[factor]
+                bar = "█" * min(count, 30)
+                print(f"    {factor:3d}x: {count:5d} {bar}")
+
+        print("\n" + "=" * 70)
+    @staticmethod
+    def export_to_csv(report: DatabaseReport, output_path: str):
+        """导出详细结果到CSV"""
+        import csv
+
+        with open(output_path, 'w', newline='', encoding='utf-8') as f:
+            writer = csv.writer(f)
+
+            # 写入表头
+            headers = [
+                'file_name', 'is_valid', 'contains_target_cation',
+                'anion_types', 'anion_mode', 'has_oxidation_states',
+                'has_partial_occupancy', 'cation_partial_occupancy',
+                'anion_partial_occupancy', 'needs_expansion',
+                'is_binary', 'has_water', 'has_radioactive',
+                'can_process', 'skip_reason'
+            ]
+            writer.writerow(headers)
+
+            # 写入数据
+            for info in report.all_structures:
+                row = [
+                    info.file_name,
+                    info.is_valid,
+                    info.contains_target_cation,
+                    '+'.join(sorted(info.anion_types)) if info.anion_types else '',
+                    info.anion_mode,
+                    info.has_oxidation_states,
+                    info.has_partial_occupancy,
+                    info.cation_with_other_cation,  # 修复：使用正确的属性名
+                    info.anion_has_partial_occupancy,
+                    info.needs_expansion,
+                    info.is_binary_compound,
+                    info.has_water_molecule,
+                    info.has_radioactive_elements,
+                    info.can_process,
+                    info.skip_reason
+                ]
+                writer.writerow(row)
+
+        print(f"详细结果已导出到: {output_path}")

src/analysis/structure_inspector.py

@@ -0,0 +1,443 @@
+"""
+结构检查器：对单个CIF文件进行深度分析（含扩胞需求判断）
+"""
+from dataclasses import dataclass, field
+from typing import Set, Dict, List, Optional, Tuple
+from pymatgen.core import Structure
+from pymatgen.core.periodic_table import Element, Specie
+from collections import defaultdict
+from fractions import Fraction
+from functools import reduce
+import math
+import re
+import os
+
+
+@dataclass
+class OccupancyInfo:
+    """共占位信息"""
+    occupation: float  # 占据率
+    atom_serials: List[int] = field(default_factory=list)  # 原子序号
+    elements: List[str] = field(default_factory=list)  # 涉及的元素
+    numerator: int = 0  # 分子
+    denominator: int = 1  # 分母
+    involves_target_cation: bool = False  # 是否涉及目标阳离子
+    involves_anion: bool = False  # 是否涉及阴离子
+
+
+@dataclass
+class ExpansionInfo:
+    """扩胞信息"""
+    needs_expansion: bool = False  # 是否需要扩胞
+    expansion_factor: int = 1  # 扩胞因子（最小公倍数）
+    occupancy_details: List[OccupancyInfo] = field(default_factory=list)  # 共占位详情
+    problematic_sites: int = 0  # 问题位点数
+    can_expand: bool = True  # 是否可以扩胞处理
+    skip_reason: str = ""  # 无法扩胞的原因
+
+
+@dataclass
+class StructureInfo:
+    """单个结构的分析结果"""
+    file_path: str
+    file_name: str
+
+    # 基础信息
+    is_valid: bool = False
+    error_message: str = ""
+
+    # 元素组成
+    elements: Set[str] = field(default_factory=set)
+    num_sites: int = 0
+    formula: str = ""
+
+    # 阳离子/阴离子信息
+    contains_target_cation: bool = False
+    anion_types: Set[str] = field(default_factory=set)
+    anion_mode: str = ""  # "single", "mixed", "none"
+
+    # 数据质量标记
+    has_oxidation_states: bool = False
+    has_partial_occupancy: bool = False  # 是否有共占位
+    has_water_molecule: bool = False
+    has_radioactive_elements: bool = False
+    is_binary_compound: bool = False
+
+    # 共占位详细分析（新增）
+    cation_with_vacancy: bool = False           # Li与空位共占位（不需处理）
+    cation_with_other_cation: bool = False      # Li与其他阳离子共占位（需扩胞）
+    anion_has_partial_occupancy: bool = False   # 阴离子共占位
+    other_has_partial_occupancy: bool = False   # 其他元素共占位（需扩胞）
+    expansion_info: ExpansionInfo = field(default_factory=ExpansionInfo)
+
+    # 可处理性
+    needs_expansion: bool = False
+    can_process: bool = False
+    skip_reason: str = ""
+
+
+class StructureInspector:
+    """结构检查器（含扩胞分析）"""
+
+    # 预定义的阴离子集合
+    VALID_ANIONS = {'O', 'S', 'Cl', 'Br'}
+
+    # 放射性元素
+    RADIOACTIVE_ELEMENTS = {
+        'U', 'Th', 'Pu', 'Ra', 'Rn', 'Po', 'Np', 'Am',
+        'Cm', 'Bk', 'Cf', 'Es', 'Fm', 'Md', 'No', 'Lr'
+    }
+
+    # 扩胞精度模式
+    PRECISION_LIMITS = {
+        'high': None,      # 精确分数
+        'normal': 100,     # 分母≤100
+        'low': 10,         # 分母≤10
+        'very_low': 5      # 分母≤5
+    }
+
+    def __init__(
+        self,
+        target_cation: str = "Li",
+        target_anions: Set[str] = None,
+        expansion_precision: str = "low"
+    ):
+        """
+        初始化检查器
+
+        Args:
+            target_cation: 目标阳离子 (如 "Li", "Na")
+            target_anions: 目标阴离子集合 (如 {"O", "S"})
+            expansion_precision: 扩胞计算精度 ('high', 'normal', 'low', 'very_low')
+        """
+        self.target_cation = target_cation
+        self.target_anions = target_anions or self.VALID_ANIONS
+        self.expansion_precision = expansion_precision
+
+        # 目标阳离子的各种可能表示形式
+        self.target_cation_variants = {
+            target_cation,
+            f"{target_cation}+",
+            f"{target_cation}1+",
+        }
+
+    def inspect(self, file_path: str) -> StructureInfo:
+        """分析单个CIF文件"""
+        import os
+        info = StructureInfo(
+            file_path=file_path,
+            file_name=os.path.basename(file_path)
+        )
+
+        # 尝试读取结构
+        try:
+            structure = Structure.from_file(file_path)
+        except Exception as e:
+            info.is_valid = False
+            info.error_message = f"读取CIF失败: {str(e)}"
+            return info
+
+        info.is_valid = True
+
+        try:
+            # ===== 关键修复：正确提取元素符号 =====
+            # structure.composition.elements 返回 Element 对象列表
+            # 需要用 .symbol 属性获取字符串
+            element_symbols = set()
+            for el in structure.composition.elements:
+                # el 是 Element 对象，el.symbol 是字符串如 "Li"
+                element_symbols.add(el.symbol)
+
+            info.elements = element_symbols
+            info.num_sites = structure.num_sites
+            info.formula = structure.composition.reduced_formula
+
+            # 检查是否为二元化合物
+            info.is_binary_compound = len(element_symbols) == 2
+
+            # ===== 关键修复：直接比较字符串 =====
+            info.contains_target_cation = self.target_cation in element_symbols
+
+            # 检查阴离子类型
+            info.anion_types = element_symbols.intersection(self.target_anions)
+            if len(info.anion_types) == 0:
+                info.anion_mode = "none"
+            elif len(info.anion_types) == 1:
+                info.anion_mode = "single"
+            else:
+                info.anion_mode = "mixed"
+
+            # 检查氧化态
+            info.has_oxidation_states = self._check_oxidation_states(structure)
+
+            # 检查共占位（核心分析）
+            try:
+                self._analyze_partial_occupancy(structure, info)
+            except Exception as e:
+                # 共占位分析失败，记录但继续
+                pass
+
+            # 检查水分子
+            try:
+                info.has_water_molecule = self._check_water_molecule(structure)
+            except:
+                info.has_water_molecule = False
+
+            # 检查放射性元素
+            info.has_radioactive_elements = bool(
+                info.elements.intersection(self.RADIOACTIVE_ELEMENTS)
+            )
+
+            # 判断可处理性
+            self._evaluate_processability(info)
+
+        except Exception as e:
+            # 分析过程出错，但文件本身是有效的
+            # 保留 is_valid = True，但记录错误
+            info.error_message = f"分析过程出错: {str(e)}"
+
+        return info
+
+    def _check_oxidation_states(self, structure: Structure) -> bool:
+        """检查结构是否包含氧化态信息"""
+        try:
+            for site in structure.sites:
+                for specie in site.species.keys():
+                    if isinstance(specie, Specie):
+                        return True
+            return False
+        except:
+            return False
+
+    def _get_element_from_species_string(self, species_str: str) -> str:
+        """从物种字符串提取纯元素符号"""
+        match = re.match(r'([A-Z][a-z]?)', species_str)
+        return match.group(1) if match else ""
+
+    def _get_occupancy_from_species_string(self, species_str: str, exclude_elements: Set[str]) -> Optional[float]:
+        """
+        从物种字符串获取非目标元素的占据率
+        格式如: "Li+:0.689, Sc3+:0.311"
+        """
+        if ':' not in species_str:
+            return None
+
+        parts = [p.strip() for p in species_str.split(',')]
+        for part in parts:
+            if ':' in part:
+                element_part, occu_part = part.split(':')
+                element = self._get_element_from_species_string(element_part.strip())
+                if element and element not in exclude_elements:
+                    try:
+                        return float(occu_part.strip())
+                    except ValueError:
+                        continue
+        return None
+
+    # 在 StructureInspector 类中，替换 _analyze_partial_occupancy 方法
+
+    def _analyze_partial_occupancy(self, structure: Structure, info: StructureInfo):
+        """
+        分析共占位情况（修正版）
+
+        关键规则：
+        - Li与空位共占位 → 不需要处理（cation_with_vacancy）
+        - Li与其他阳离子共占位 → 需要扩胞（cation_with_other_cation）
+        - 阴离子共占位 → 通常不处理
+        - 其他阳离子共占位 → 需要扩胞
+        """
+        occupancy_dict = defaultdict(list)  # {occupation: [site_indices]}
+        occupancy_elements = {}  # {occupation: [elements]}
+
+        for i, site in enumerate(structure.sites):
+            site_species = site.species
+            species_string = str(site.species)
+
+            # 提取各元素及其占据率
+            species_occu = {}  # {element: occupancy}
+            for sp, occu in site_species.items():
+                elem = sp.symbol if hasattr(sp, 'symbol') else str(sp)
+                elem = self._get_element_from_species_string(elem)
+                if elem:
+                    species_occu[elem] = occu
+
+            total_occupancy = sum(species_occu.values())
+            elements_at_site = list(species_occu.keys())
+
+            # 检查是否有部分占据
+            has_partial = any(occu < 1.0 for occu in species_occu.values()) or len(species_occu) > 1
+
+            if not has_partial:
+                continue
+
+            info.has_partial_occupancy = True
+
+            # 判断Li的共占位情况
+            if self.target_cation in elements_at_site:
+                li_occu = species_occu.get(self.target_cation, 0)
+                other_elements = [e for e in elements_at_site if e != self.target_cation]
+
+                if not other_elements and li_occu < 1.0:
+                    # Li与空位共占位（Li占据率<1，但没有其他元素）
+                    info.cation_with_vacancy = True
+                elif other_elements:
+                    # Li与其他元素共占位
+                    other_are_anions = all(e in self.target_anions for e in other_elements)
+                    if other_are_anions:
+                        # Li与阴离子共占位（罕见，标记为阴离子共占位）
+                        info.anion_has_partial_occupancy = True
+                    else:
+                        # Li与其他阳离子共占位 → 需要扩胞
+                        info.cation_with_other_cation = True
+
+                        # 记录需要扩胞的占据率（取非Li元素的占据率）
+                        for elem in other_elements:
+                            if elem not in self.target_anions:
+                                occu = species_occu.get(elem, 0)
+                                if occu > 0 and occu < 1.0:
+                                    occupancy_dict[occu].append(i)
+                                    occupancy_elements[occu] = elements_at_site
+            else:
+                # 不涉及Li的位点
+                # 判断是否涉及阴离子
+                if any(elem in self.target_anions for elem in elements_at_site):
+                    info.anion_has_partial_occupancy = True
+                else:
+                    # 其他阳离子的共占位 → 需要扩胞
+                    info.other_has_partial_occupancy = True
+
+                    # 获取占据率
+                    for elem, occu in species_occu.items():
+                        if occu > 0 and occu < 1.0:
+                            occupancy_dict[occu].append(i)
+                            occupancy_elements[occu] = elements_at_site
+                            break  # 只记录一次
+
+        # 计算扩胞信息
+        self._calculate_expansion_info(info, occupancy_dict, occupancy_elements)
+
+    def _evaluate_processability(self, info: StructureInfo):
+        """评估可处理性（修正版）"""
+        skip_reasons = []
+
+        if not info.is_valid:
+            skip_reasons.append("无法解析CIF文件")
+
+        if not info.contains_target_cation:
+            skip_reasons.append(f"不含{self.target_cation}")
+
+        if info.anion_mode == "none":
+            skip_reasons.append("不含目标阴离子")
+
+        if info.is_binary_compound:
+            skip_reasons.append("二元化合物")
+
+        if info.has_radioactive_elements:
+            skip_reasons.append("含放射性元素")
+
+        # Li与空位共占位 → 不需要处理（不加入skip_reasons）
+        # info.cation_with_vacancy 不影响可处理性
+
+        # Li与其他阳离子共占位 → 需要扩胞（如果扩胞因子合理则可处理）
+        if info.cation_with_other_cation:
+            if info.expansion_info.can_expand:
+                info.needs_expansion = True
+            else:
+                skip_reasons.append(f"{self.target_cation}与其他阳离子共占位且{info.expansion_info.skip_reason}")
+
+        # 阴离子共占位 → 不处理
+        if info.anion_has_partial_occupancy:
+            skip_reasons.append("阴离子存在共占位")
+
+        if info.has_water_molecule:
+            skip_reasons.append("含水分子")
+
+        # 其他阳离子共占位（不涉及Li）→ 需要扩胞
+        if info.other_has_partial_occupancy:
+            if info.expansion_info.can_expand:
+                info.needs_expansion = True
+            else:
+                skip_reasons.append(info.expansion_info.skip_reason)
+
+        if skip_reasons:
+            info.can_process = False
+            info.skip_reason = "; ".join(skip_reasons)
+        else:
+            info.can_process = True
+
+    def _calculate_expansion_info(
+        self,
+        info: StructureInfo,
+        occupancy_dict: Dict[float, List[int]],
+        occupancy_elements: Dict[float, List[str]]
+    ):
+        """计算扩胞相关信息"""
+        expansion_info = ExpansionInfo()
+
+        if not occupancy_dict:
+            info.expansion_info = expansion_info
+            return
+
+        # 需要扩胞（有非目标阳离子的共占位）
+        expansion_info.needs_expansion = True
+        expansion_info.problematic_sites = sum(len(v) for v in occupancy_dict.values())
+
+        # 转换为OccupancyInfo列表
+        occupancy_list = []
+        for occu, serials in occupancy_dict.items():
+            elements = occupancy_elements.get(occu, [])
+
+            # 根据精度计算分数
+            limit = self.PRECISION_LIMITS.get(self.expansion_precision)
+            if limit:
+                fraction = Fraction(occu).limit_denominator(limit)
+            else:
+                fraction = Fraction(occu).limit_denominator()
+
+            occ_info = OccupancyInfo(
+                occupation=occu,
+                atom_serials=[s + 1 for s in serials],  # 转为1-based
+                elements=elements,
+                numerator=fraction.numerator,
+                denominator=fraction.denominator,
+                involves_target_cation=self.target_cation in elements,
+                involves_anion=any(e in self.target_anions for e in elements)
+            )
+            occupancy_list.append(occ_info)
+
+        expansion_info.occupancy_details = occupancy_list
+
+        # 计算最小公倍数（扩胞因子）
+        denominators = [occ.denominator for occ in occupancy_list]
+        if denominators:
+            lcm = reduce(lambda a, b: a * b // math.gcd(a, b), denominators, 1)
+            expansion_info.expansion_factor = lcm
+
+            # 判断是否可以扩胞（因子过大则不可处理）
+            if lcm > 64:  # 扩胞超过64倍通常不可行
+                expansion_info.can_expand = False
+                expansion_info.skip_reason = f"扩胞因子过大({lcm})"
+
+        info.expansion_info = expansion_info
+        info.needs_expansion = expansion_info.needs_expansion and expansion_info.can_expand
+
+    def _check_water_molecule(self, structure: Structure) -> bool:
+        """检查是否含有水分子"""
+        try:
+            oxygen_sites = []
+            hydrogen_sites = []
+
+            for site in structure.sites:
+                species_str = str(site.species)
+                if 'O' in species_str:
+                    oxygen_sites.append(site)
+                if 'H' in species_str:
+                    hydrogen_sites.append(site)
+
+            for o_site in oxygen_sites:
+                nearby_h = [h for h in hydrogen_sites if o_site.distance(h) < 1.2]
+                if len(nearby_h) >= 2:
+                    return True
+            return False
+        except:
+            return False

src/analysis/worker.py

@@ -0,0 +1,199 @@
+"""
+工作进程：处理单个分析任务
+设计为可以独立运行（用于SLURM作业数组）
+"""
+import os
+import pickle
+from typing import List, Tuple, Optional
+from dataclasses import asdict, fields
+
+from .structure_inspector import StructureInspector, StructureInfo
+
+
+def analyze_single_file(args: Tuple[str, str, set]) -> Optional[StructureInfo]:
+    """
+    分析单个CIF文件（Worker函数）
+
+    Args:
+        args: (file_path, target_cation, target_anions)
+
+    Returns:
+        StructureInfo 或 None（如果分析失败）
+    """
+    file_path, target_cation, target_anions = args
+
+    try:
+        inspector = StructureInspector(
+            target_cation=target_cation,
+            target_anions=target_anions
+        )
+        result = inspector.inspect(file_path)
+        return result
+    except Exception as e:
+        # 返回一个标记失败的结果（而不是 None）
+        return StructureInfo(
+            file_path=file_path,
+            file_name=os.path.basename(file_path),
+            is_valid=False,
+            error_message=f"Worker异常: {str(e)}"
+        )
+
+def structure_info_to_dict(info: StructureInfo) -> dict:
+    """
+    将 StructureInfo 转换为可序列化的字典
+    处理 set、dataclass 等特殊类型
+    """
+    result = {}
+    for field in fields(info):
+        value = getattr(info, field.name)
+
+        # 处理 set 类型
+        if isinstance(value, set):
+            result[field.name] = list(value)
+        # 处理嵌套的 dataclass (如 ExpansionInfo)
+        elif hasattr(value, '__dataclass_fields__'):
+            result[field.name] = asdict(value)
+        # 处理 list 中可能包含的 dataclass
+        elif isinstance(value, list):
+            result[field.name] = [
+                asdict(item) if hasattr(item, '__dataclass_fields__') else item
+                for item in value
+            ]
+        else:
+            result[field.name] = value
+
+    return result
+
+
+def dict_to_structure_info(d: dict) -> StructureInfo:
+    """
+    从字典恢复 StructureInfo 对象
+    """
+    from .structure_inspector import ExpansionInfo, OccupancyInfo
+
+    # 处理 set 类型字段
+    if 'elements' in d and isinstance(d['elements'], list):
+        d['elements'] = set(d['elements'])
+    if 'anion_types' in d and isinstance(d['anion_types'], list):
+        d['anion_types'] = set(d['anion_types'])
+    if 'target_anions' in d and isinstance(d['target_anions'], list):
+        d['target_anions'] = set(d['target_anions'])
+
+    # 处理 ExpansionInfo
+    if 'expansion_info' in d and isinstance(d['expansion_info'], dict):
+        exp_dict = d['expansion_info']
+
+        # 处理 OccupancyInfo 列表
+        if 'occupancy_details' in exp_dict:
+            exp_dict['occupancy_details'] = [
+                OccupancyInfo(**occ) if isinstance(occ, dict) else occ
+                for occ in exp_dict['occupancy_details']
+            ]
+
+        d['expansion_info'] = ExpansionInfo(**exp_dict)
+
+    return StructureInfo(**d)
+
+
+def batch_analyze(
+    file_paths: List[str],
+    target_cation: str,
+    target_anions: set,
+    output_file: str = None
+) -> List[StructureInfo]:
+    """
+    批量分析文件（用于SLURM子任务）
+
+    Args:
+        file_paths: CIF文件路径列表
+        target_cation: 目标阳离子
+        target_anions: 目标阴离子集合
+        output_file: 输出文件路径（pickle格式）
+
+    Returns:
+        StructureInfo列表
+    """
+    results = []
+
+    inspector = StructureInspector(
+        target_cation=target_cation,
+        target_anions=target_anions
+    )
+
+    for file_path in file_paths:
+        try:
+            info = inspector.inspect(file_path)
+            results.append(info)
+        except Exception as e:
+            results.append(StructureInfo(
+                file_path=file_path,
+                file_name=os.path.basename(file_path),
+                is_valid=False,
+                error_message=str(e)
+            ))
+
+    # 保存结果
+    if output_file:
+        serializable_results = [structure_info_to_dict(r) for r in results]
+        with open(output_file, 'wb') as f:
+            pickle.dump(serializable_results, f)
+
+    return results
+
+
+def load_results(result_file: str) -> List[StructureInfo]:
+    """
+    从pickle文件加载结果
+    """
+    with open(result_file, 'rb') as f:
+        data = pickle.load(f)
+
+    return [dict_to_structure_info(d) for d in data]
+
+
+def merge_results(result_files: List[str]) -> List[StructureInfo]:
+    """
+    合并多个结果文件（用于汇总SLURM作业数组的输出）
+    """
+    all_results = []
+    for f in result_files:
+        if os.path.exists(f):
+            all_results.extend(load_results(f))
+    return all_results
+
+
+# 用于SLURM作业数组的命令行入口
+if __name__ == "__main__":
+    import argparse
+    import json
+
+    parser = argparse.ArgumentParser(description="CIF Analysis Worker")
+    parser.add_argument("--tasks-file", required=True, help="任务文件路径(JSON)")
+    parser.add_argument("--output-dir", required=True, help="输出目录")
+    parser.add_argument("--task-id", type=int, default=0, help="任务ID(用于数组作业)")
+    parser.add_argument("--num-workers", type=int, default=1, help="并行worker数")
+
+    args = parser.parse_args()
+
+    # 加载任务
+    with open(args.tasks_file, 'r') as f:
+        task_config = json.load(f)
+
+    file_paths = task_config['files']
+    target_cation = task_config['target_cation']
+    target_anions = set(task_config['target_anions'])
+
+    # 如果是数组作业，只处理分配的部分
+    if args.task_id >= 0:
+        chunk_size = len(file_paths) // args.num_workers + 1
+        start_idx = args.task_id * chunk_size
+        end_idx = min(start_idx + chunk_size, len(file_paths))
+        file_paths = file_paths[start_idx:end_idx]
+
+    # 输出文件
+    output_file = os.path.join(args.output_dir, f"results_{args.task_id}.pkl")
+
+    # 执行分析
+    print(f"Worker {args.task_id}: 处理 {len(file_paths)} 个文件")
+    results = batch_analyze(file_paths, target_cation, target_anions, output_file)
+    print(f"Worker {args.task_id}: 完成，结果保存到 {output_file}")

src/computation/__init__.py

@@ -0,0 +1,15 @@
+"""
+计算模块：Zeo++ Voronoi 分析
+"""
+from .workspace_manager import WorkspaceManager
+from .zeo_executor import ZeoExecutor, ZeoConfig
+from .result_processor import ResultProcessor, FilterCriteria, StructureResult
+
+__all__ = [
+    'WorkspaceManager',
+    'ZeoExecutor',
+    'ZeoConfig',
+    'ResultProcessor',
+    'FilterCriteria',
+    'StructureResult'
+]

src/computation/result_processor.py

@@ -0,0 +1,426 @@
+"""
+Zeo++ 计算结果处理器：提取数据、筛选结构
+"""
+import os
+import re
+import shutil
+from typing import Dict, List, Optional, Tuple
+from dataclasses import dataclass, field
+import pandas as pd
+
+
+@dataclass
+class FilterCriteria:
+    """筛选条件"""
+    min_percolation_diameter: float = 1.0  # 最小渗透直径 (Å)，默认 1.0
+    min_d_value: float = 2.0               # 最小 d 值，默认 2.0
+    max_node_length: float = float('inf')  # 最大节点长度 (Å)
+
+
+@dataclass
+class StructureResult:
+    """单个结构的计算结果"""
+    structure_name: str
+    anion_type: str
+    work_dir: str
+    
+    # 提取的参数
+    percolation_diameter: Optional[float] = None
+    min_d: Optional[float] = None
+    max_node_length: Optional[float] = None
+    
+    # 筛选结果
+    passed_filter: bool = False
+    filter_reason: str = ""
+
+
+class ResultProcessor:
+    """
+    Zeo++ 计算结果处理器
+    
+    功能：
+    1. 从每个结构目录的 log.txt 提取关键参数
+    2. 汇总所有结果到 CSV 文件
+    3. 根据筛选条件筛选结构
+    4. 将通过筛选的结构复制到新文件夹
+    """
+    
+    def __init__(
+        self,
+        workspace_path: str = "workspace",
+        data_dir: str = None,
+        output_dir: str = None
+    ):
+        """
+        初始化结果处理器
+        
+        Args:
+            workspace_path: 工作区根目录
+            data_dir: 数据目录（默认 workspace/data）
+            output_dir: 输出目录（默认 workspace/results）
+        """
+        self.workspace_path = os.path.abspath(workspace_path)
+        self.data_dir = data_dir or os.path.join(self.workspace_path, "data")
+        self.output_dir = output_dir or os.path.join(self.workspace_path, "results")
+        
+    def extract_from_log(self, log_path: str) -> Tuple[Optional[float], Optional[float], Optional[float]]:
+        """
+        从 log.txt 中提取三个关键参数
+        
+        Args:
+            log_path: log.txt 文件路径
+            
+        Returns:
+            (percolation_diameter, min_d, max_node_length)
+        """
+        if not os.path.exists(log_path):
+            return None, None, None
+        
+        try:
+            with open(log_path, 'r', encoding='utf-8') as f:
+                content = f.read()
+        except Exception:
+            return None, None, None
+        
+        # 正则表达式 - 与 py/extract_data.py 保持一致
+        
+        # 1. Percolation diameter: "#     Percolation diameter (A): 1.06"
+        re_percolation = r"Percolation diameter \(A\):\s*([\d\.]+)"
+        
+        # 2. Minimum of d: "the minium of d\n3.862140561244235"
+        #    注意：这是 Topological_Analysis 库输出的格式
+        re_min_d = r"the minium of d\s*\n\s*([\d\.]+)"
+        
+        # 3. Maximum node length: "#     Maximum node length detected: 1.332 A"
+        re_max_node = r"Maximum node length detected:\s*([\d\.]+)\s*A"
+        
+        # 提取数据
+        match_perc = re.search(re_percolation, content)
+        match_d = re.search(re_min_d, content)
+        match_node = re.search(re_max_node, content)
+        
+        val_perc = float(match_perc.group(1)) if match_perc else None
+        val_d = float(match_d.group(1)) if match_d else None
+        val_node = float(match_node.group(1)) if match_node else None
+        
+        return val_perc, val_d, val_node
+    
+    def process_all_structures(self) -> List[StructureResult]:
+        """
+        处理所有结构，提取计算结果
+        
+        Returns:
+            StructureResult 列表
+        """
+        results = []
+        
+        if not os.path.exists(self.data_dir):
+            print(f"⚠️ 数据目录不存在: {self.data_dir}")
+            return results
+        
+        print("\n正在提取计算结果...")
+        
+        # 遍历阴离子目录
+        for anion_key in os.listdir(self.data_dir):
+            anion_dir = os.path.join(self.data_dir, anion_key)
+            if not os.path.isdir(anion_dir):
+                continue
+            
+            # 遍历结构目录
+            for struct_name in os.listdir(anion_dir):
+                struct_dir = os.path.join(anion_dir, struct_name)
+                if not os.path.isdir(struct_dir):
+                    continue
+                
+                # 查找 log.txt
+                log_path = os.path.join(struct_dir, "log.txt")
+                
+                # 提取参数
+                perc, min_d, max_node = self.extract_from_log(log_path)
+                
+                result = StructureResult(
+                    structure_name=struct_name,
+                    anion_type=anion_key,
+                    work_dir=struct_dir,
+                    percolation_diameter=perc,
+                    min_d=min_d,
+                    max_node_length=max_node
+                )
+                
+                results.append(result)
+        
+        print(f"  共处理 {len(results)} 个结构")
+        return results
+    
+    def apply_filter(
+        self,
+        results: List[StructureResult],
+        criteria: FilterCriteria
+    ) -> List[StructureResult]:
+        """
+        应用筛选条件
+        
+        Args:
+            results: 结构结果列表
+            criteria: 筛选条件
+            
+        Returns:
+            更新后的结果列表（包含筛选状态）
+        """
+        print("\n应用筛选条件...")
+        print(f"  最小渗透直径: {criteria.min_percolation_diameter} Å")
+        print(f"  最小 d 值:    {criteria.min_d_value}")
+        print(f"  最大节点长度: {criteria.max_node_length} Å")
+        
+        passed_count = 0
+        
+        for result in results:
+            # 检查是否有有效数据
+            if result.percolation_diameter is None or result.min_d is None:
+                result.passed_filter = False
+                result.filter_reason = "数据缺失"
+                continue
+            
+            # 检查渗透直径
+            if result.percolation_diameter < criteria.min_percolation_diameter:
+                result.passed_filter = False
+                result.filter_reason = f"渗透直径 {result.percolation_diameter:.3f} < {criteria.min_percolation_diameter}"
+                continue
+            
+            # 检查 d 值
+            if result.min_d < criteria.min_d_value:
+                result.passed_filter = False
+                result.filter_reason = f"d 值 {result.min_d:.3f} < {criteria.min_d_value}"
+                continue
+            
+            # 检查节点长度（如果有数据）
+            if result.max_node_length is not None:
+                if result.max_node_length > criteria.max_node_length:
+                    result.passed_filter = False
+                    result.filter_reason = f"节点长度 {result.max_node_length:.3f} > {criteria.max_node_length}"
+                    continue
+            
+            # 通过所有筛选
+            result.passed_filter = True
+            result.filter_reason = "通过"
+            passed_count += 1
+        
+        print(f"  通过筛选: {passed_count}/{len(results)}")
+        return results
+    
+    def save_summary_csv(
+        self,
+        results: List[StructureResult],
+        output_path: str = None
+    ) -> str:
+        """
+        保存汇总 CSV 文件
+        
+        Args:
+            results: 结构结果列表
+            output_path: 输出路径（默认 workspace/results/summary.csv）
+            
+        Returns:
+            CSV 文件路径
+        """
+        if output_path is None:
+            os.makedirs(self.output_dir, exist_ok=True)
+            output_path = os.path.join(self.output_dir, "summary.csv")
+        
+        # 构建数据
+        data = []
+        for r in results:
+            data.append({
+                'Structure': r.structure_name,
+                'Anion_Type': r.anion_type,
+                'Percolation_Diameter_A': r.percolation_diameter,
+                'Min_d': r.min_d,
+                'Max_Node_Length_A': r.max_node_length,
+                'Passed_Filter': r.passed_filter,
+                'Filter_Reason': r.filter_reason
+            })
+        
+        df = pd.DataFrame(data)
+        
+        # 按阴离子类型和结构名排序
+        df = df.sort_values(['Anion_Type', 'Structure'])
+        
+        # 保存
+        os.makedirs(os.path.dirname(output_path), exist_ok=True)
+        df.to_csv(output_path, index=False)
+        
+        print(f"\n汇总 CSV 已保存: {output_path}")
+        return output_path
+    
+    def save_anion_csv(
+        self,
+        results: List[StructureResult],
+        output_dir: str = None
+    ) -> List[str]:
+        """
+        按阴离子类型分别保存 CSV 文件
+        
+        Args:
+            results: 结构结果列表
+            output_dir: 输出目录
+            
+        Returns:
+            生成的 CSV 文件路径列表
+        """
+        if output_dir is None:
+            output_dir = self.output_dir
+        
+        # 按阴离子类型分组
+        anion_groups: Dict[str, List[StructureResult]] = {}
+        for r in results:
+            if r.anion_type not in anion_groups:
+                anion_groups[r.anion_type] = []
+            anion_groups[r.anion_type].append(r)
+        
+        csv_files = []
+        
+        for anion_type, group_results in anion_groups.items():
+            # 构建数据
+            data = []
+            for r in group_results:
+                data.append({
+                    'Structure': r.structure_name,
+                    'Percolation_Diameter_A': r.percolation_diameter,
+                    'Min_d': r.min_d,
+                    'Max_Node_Length_A': r.max_node_length,
+                    'Passed_Filter': r.passed_filter,
+                    'Filter_Reason': r.filter_reason
+                })
+            
+            df = pd.DataFrame(data)
+            df = df.sort_values('Structure')
+            
+            # 保存到对应目录
+            anion_output_dir = os.path.join(output_dir, anion_type)
+            os.makedirs(anion_output_dir, exist_ok=True)
+            
+            csv_path = os.path.join(anion_output_dir, f"{anion_type}.csv")
+            df.to_csv(csv_path, index=False)
+            csv_files.append(csv_path)
+            
+            print(f"  {anion_type}: {len(group_results)} 个结构 -> {csv_path}")
+        
+        return csv_files
+    
+    def copy_passed_structures(
+        self,
+        results: List[StructureResult],
+        output_dir: str = None
+    ) -> int:
+        """
+        将通过筛选的结构复制到新文件夹
+        
+        Args:
+            results: 结构结果列表
+            output_dir: 输出目录（默认 workspace/passed）
+            
+        Returns:
+            复制的结构数量
+        """
+        if output_dir is None:
+            output_dir = os.path.join(self.workspace_path, "passed")
+        
+        passed_results = [r for r in results if r.passed_filter]
+        
+        if not passed_results:
+            print("\n没有通过筛选的结构")
+            return 0
+        
+        print(f"\n正在复制 {len(passed_results)} 个通过筛选的结构...")
+        
+        copied = 0
+        for r in passed_results:
+            # 目标目录：passed/阴离子类型/结构名/
+            dst_dir = os.path.join(output_dir, r.anion_type, r.structure_name)
+            
+            try:
+                # 如果目标已存在，先删除
+                if os.path.exists(dst_dir):
+                    shutil.rmtree(dst_dir)
+                
+                # 复制整个目录
+                shutil.copytree(r.work_dir, dst_dir)
+                copied += 1
+                
+            except Exception as e:
+                print(f"  ⚠️ 复制失败 {r.structure_name}: {e}")
+        
+        print(f"  已复制 {copied} 个结构到: {output_dir}")
+        return copied
+    
+    def process_and_filter(
+        self,
+        criteria: FilterCriteria = None,
+        save_csv: bool = True,
+        copy_passed: bool = True
+    ) -> Tuple[List[StructureResult], Dict]:
+        """
+        完整的处理流程：提取数据 -> 筛选 -> 保存 CSV -> 复制通过的结构
+        
+        Args:
+            criteria: 筛选条件（如果为 None，则不筛选）
+            save_csv: 是否保存 CSV
+            copy_passed: 是否复制通过筛选的结构
+            
+        Returns:
+            (结果列表, 统计信息字典)
+        """
+        # 1. 提取所有结构的计算结果
+        results = self.process_all_structures()
+        
+        if not results:
+            return results, {'total': 0, 'passed': 0, 'failed': 0}
+        
+        # 2. 应用筛选条件
+        if criteria is not None:
+            results = self.apply_filter(results, criteria)
+        
+        # 3. 保存 CSV
+        if save_csv:
+            print("\n保存结果 CSV...")
+            self.save_summary_csv(results)
+            self.save_anion_csv(results)
+        
+        # 4. 复制通过筛选的结构
+        if copy_passed and criteria is not None:
+            self.copy_passed_structures(results)
+        
+        # 统计
+        stats = {
+            'total': len(results),
+            'passed': sum(1 for r in results if r.passed_filter),
+            'failed': sum(1 for r in results if not r.passed_filter),
+            'missing_data': sum(1 for r in results if r.filter_reason == "数据缺失")
+        }
+        
+        return results, stats
+    
+    def print_summary(self, results: List[StructureResult], stats: Dict):
+        """打印结果摘要"""
+        print("\n" + "=" * 60)
+        print("【计算结果摘要】")
+        print("=" * 60)
+        print(f"  总结构数:     {stats['total']}")
+        print(f"  通过筛选:     {stats['passed']}")
+        print(f"  未通过筛选:   {stats['failed']}")
+        print(f"  数据缺失:     {stats.get('missing_data', 0)}")
+        
+        # 按阴离子类型统计
+        anion_stats: Dict[str, Dict] = {}
+        for r in results:
+            if r.anion_type not in anion_stats:
+                anion_stats[r.anion_type] = {'total': 0, 'passed': 0}
+            anion_stats[r.anion_type]['total'] += 1
+            if r.passed_filter:
+                anion_stats[r.anion_type]['passed'] += 1
+        
+        print("\n  按阴离子类型:")
+        for anion, s in sorted(anion_stats.items()):
+            print(f"    {anion}: {s['passed']}/{s['total']} 通过")
+        
+        print("=" * 60)

src/computation/workspace_manager.py

@@ -0,0 +1,288 @@
+"""
+工作区管理器：管理计算工作区的创建和软链接
+"""
+import os
+import shutil
+from pathlib import Path
+from typing import Dict, List, Optional, Set, Tuple
+from dataclasses import dataclass, field
+
+
+@dataclass
+class WorkspaceInfo:
+    """工作区信息"""
+    workspace_path: str
+    data_dir: str  # workspace/data
+    tool_dir: str  # tool 目录
+    target_cation: str
+    target_anions: Set[str]
+    
+    # 统计信息
+    total_structures: int = 0
+    anion_counts: Dict[str, int] = field(default_factory=dict)
+    linked_structures: int = 0  # 已创建软链接的结构数
+
+
+class WorkspaceManager:
+    """
+    工作区管理器
+    
+    负责：
+    1. 检测现有工作区
+    2. 创建软链接（yaml 配置文件和计算脚本放在每个结构目录下）
+    3. 准备计算任务
+    """
+    
+    # 支持的阴离子及其配置文件
+    SUPPORTED_ANIONS = {'O', 'S', 'Cl', 'Br'}
+    
+    def __init__(
+        self,
+        workspace_path: str = "workspace",
+        tool_dir: str = "tool",
+        target_cation: str = "Li"
+    ):
+        """
+        初始化工作区管理器
+        
+        Args:
+            workspace_path: 工作区根目录
+            tool_dir: 工具目录（包含 yaml 配置和计算脚本）
+            target_cation: 目标阳离子
+        """
+        self.workspace_path = os.path.abspath(workspace_path)
+        self.tool_dir = os.path.abspath(tool_dir)
+        self.target_cation = target_cation
+        
+        # 数据目录
+        self.data_dir = os.path.join(self.workspace_path, "data")
+        
+    def check_existing_workspace(self) -> Optional[WorkspaceInfo]:
+        """
+        检查现有工作区
+        
+        Returns:
+            WorkspaceInfo 如果存在，否则 None
+        """
+        if not os.path.exists(self.data_dir):
+            return None
+            
+        # 扫描数据目录
+        anion_counts = {}
+        total = 0
+        linked = 0
+        
+        for item in os.listdir(self.data_dir):
+            item_path = os.path.join(self.data_dir, item)
+            if os.path.isdir(item_path):
+                # 可能是阴离子目录（如 O, S, O+S）
+                # 统计其中的结构数量
+                count = 0
+                for sub_item in os.listdir(item_path):
+                    sub_path = os.path.join(item_path, sub_item)
+                    if os.path.isdir(sub_path):
+                        # 检查是否包含 CIF 文件
+                        cif_files = [f for f in os.listdir(sub_path) if f.endswith('.cif')]
+                        if cif_files:
+                            count += 1
+                            # 检查是否已有软链接
+                            yaml_files = [f for f in os.listdir(sub_path) if f.endswith('.yaml')]
+                            if yaml_files:
+                                linked += 1
+                
+                if count > 0:
+                    anion_counts[item] = count
+                    total += count
+        
+        if total == 0:
+            return None
+        
+        return WorkspaceInfo(
+            workspace_path=self.workspace_path,
+            data_dir=self.data_dir,
+            tool_dir=self.tool_dir,
+            target_cation=self.target_cation,
+            target_anions=set(anion_counts.keys()),
+            total_structures=total,
+            anion_counts=anion_counts,
+            linked_structures=linked
+        )
+    
+    def setup_workspace(
+        self,
+        target_anions: Set[str] = None,
+        force_relink: bool = False
+    ) -> WorkspaceInfo:
+        """
+        设置工作区：在每个结构目录下创建软链接
+        
+        软链接规则：
+        - yaml 文件：使用与阴离子目录同名的 yaml（如 O 目录用 O.yaml，Cl+O 目录用 Cl+O.yaml）
+        - python 脚本：analyze_voronoi_nodes.py
+        
+        Args:
+            target_anions: 目标阴离子集合
+            force_relink: 是否强制重新创建软链接
+            
+        Returns:
+            WorkspaceInfo
+        """
+        if target_anions is None:
+            target_anions = self.SUPPORTED_ANIONS
+            
+        # 确保数据目录存在
+        if not os.path.exists(self.data_dir):
+            raise FileNotFoundError(f"数据目录不存在: {self.data_dir}")
+        
+        # 获取计算脚本路径
+        analyze_script = os.path.join(self.tool_dir, "analyze_voronoi_nodes.py")
+        if not os.path.exists(analyze_script):
+            raise FileNotFoundError(f"计算脚本不存在: {analyze_script}")
+        
+        anion_counts = {}
+        total = 0
+        linked = 0
+        
+        print("\n正在设置工作区软链接...")
+        
+        # 遍历数据目录中的阴离子子目录
+        for anion_key in os.listdir(self.data_dir):
+            anion_dir = os.path.join(self.data_dir, anion_key)
+            if not os.path.isdir(anion_dir):
+                continue
+            
+            # 确定使用哪个 yaml 配置文件
+            # 使用与阴离子目录同名的 yaml 文件（如 O.yaml, Cl+O.yaml）
+            yaml_name = f"{anion_key}.yaml"
+            yaml_source = os.path.join(self.tool_dir, self.target_cation, yaml_name)
+            
+            if not os.path.exists(yaml_source):
+                print(f"  ⚠️ 配置文件不存在: {yaml_source}")
+                continue
+            
+            # 统计并处理该阴离子目录下的所有结构
+            count = 0
+            for struct_name in os.listdir(anion_dir):
+                struct_dir = os.path.join(anion_dir, struct_name)
+                
+                if not os.path.isdir(struct_dir):
+                    continue
+                
+                # 检查是否包含 CIF 文件
+                cif_files = [f for f in os.listdir(struct_dir) if f.endswith('.cif')]
+                if not cif_files:
+                    continue
+                
+                count += 1
+                
+                # 在结构目录下创建软链接
+                yaml_link = os.path.join(struct_dir, yaml_name)
+                script_link = os.path.join(struct_dir, "analyze_voronoi_nodes.py")
+                
+                # 创建 yaml 软链接
+                if os.path.exists(yaml_link) or os.path.islink(yaml_link):
+                    if force_relink:
+                        os.remove(yaml_link)
+                        os.symlink(yaml_source, yaml_link)
+                        linked += 1
+                else:
+                    os.symlink(yaml_source, yaml_link)
+                    linked += 1
+                
+                # 创建计算脚本软链接
+                if os.path.exists(script_link) or os.path.islink(script_link):
+                    if force_relink:
+                        os.remove(script_link)
+                        os.symlink(analyze_script, script_link)
+                else:
+                    os.symlink(analyze_script, script_link)
+            
+            if count > 0:
+                anion_counts[anion_key] = count
+                total += count
+                print(f"  ✓ {anion_key}: {count} 个结构, 配置 -> {yaml_name}")
+        
+        print(f"\n  总计: {total} 个结构, 新建软链接: {linked}")
+        
+        return WorkspaceInfo(
+            workspace_path=self.workspace_path,
+            data_dir=self.data_dir,
+            tool_dir=self.tool_dir,
+            target_cation=self.target_cation,
+            target_anions=set(anion_counts.keys()),
+            total_structures=total,
+            anion_counts=anion_counts,
+            linked_structures=linked
+        )
+    
+    def get_computation_tasks(
+        self,
+        workspace_info: WorkspaceInfo = None
+    ) -> List[Dict]:
+        """
+        获取所有计算任务
+        
+        Returns:
+            任务列表，每个任务包含：
+            - cif_path: CIF 文件路径
+            - yaml_name: YAML 配置文件名（如 O.yaml）
+            - work_dir: 工作目录（结构目录）
+            - anion_type: 阴离子类型
+            - structure_name: 结构名称
+        """
+        if workspace_info is None:
+            workspace_info = self.check_existing_workspace()
+            
+        if workspace_info is None:
+            return []
+        
+        tasks = []
+        
+        for anion_key in workspace_info.anion_counts.keys():
+            anion_dir = os.path.join(self.data_dir, anion_key)
+            yaml_name = f"{anion_key}.yaml"
+            
+            # 遍历该阴离子目录下的所有结构
+            for struct_name in os.listdir(anion_dir):
+                struct_dir = os.path.join(anion_dir, struct_name)
+                
+                if not os.path.isdir(struct_dir):
+                    continue
+                
+                # 查找 CIF 文件
+                cif_files = [f for f in os.listdir(struct_dir) if f.endswith('.cif')]
+                
+                # 检查是否有 yaml 软链接
+                yaml_path = os.path.join(struct_dir, yaml_name)
+                if not os.path.exists(yaml_path):
+                    continue
+                
+                for cif_file in cif_files:
+                    cif_path = os.path.join(struct_dir, cif_file)
+                    
+                    tasks.append({
+                        'cif_path': cif_path,
+                        'yaml_name': yaml_name,
+                        'work_dir': struct_dir,
+                        'anion_type': anion_key,
+                        'structure_name': struct_name,
+                        'cif_name': cif_file
+                    })
+        
+        return tasks
+    
+    def print_workspace_summary(self, workspace_info: WorkspaceInfo):
+        """打印工作区摘要"""
+        print("\n" + "=" * 60)
+        print("【工作区摘要】")
+        print("=" * 60)
+        print(f"  工作区路径:   {workspace_info.workspace_path}")
+        print(f"  数据目录:     {workspace_info.data_dir}")
+        print(f"  目标阳离子:   {workspace_info.target_cation}")
+        print(f"  总结构数:     {workspace_info.total_structures}")
+        print(f"  已配置软链接: {workspace_info.linked_structures}")
+        print()
+        print("  阴离子分布:")
+        for anion, count in sorted(workspace_info.anion_counts.items()):
+            print(f"    - {anion}: {count} 个结构")
+        print("=" * 60)

src/computation/zeo_executor.py

@@ -0,0 +1,446 @@
+"""
+Zeo++ 计算执行器：使用 SLURM 作业数组高效调度大量计算任务
+"""
+import os
+import subprocess
+import time
+import json
+import tempfile
+from typing import List, Dict, Optional, Callable, Any
+from dataclasses import dataclass, field
+from enum import Enum
+import threading
+
+from ..core.progress import ProgressManager
+
+
+@dataclass
+class ZeoConfig:
+    """Zeo++ 计算配置"""
+    # 环境配置
+    conda_env: str = "/cluster/home/koko125/anaconda3/envs/zeo"
+    
+    # SLURM 配置
+    partition: str = "cpu"
+    time_limit: str = "2:00:00"  # 单个任务时间限制
+    memory_per_task: str = "4G"
+    
+    # 作业数组配置
+    max_array_size: int = 1000  # SLURM 作业数组最大大小
+    max_concurrent: int = 50    # 最大并发任务数
+    
+    # 轮询配置
+    poll_interval: float = 5.0  # 状态检查间隔（秒）
+    
+    # 过滤器配置
+    filters: List[str] = field(default_factory=lambda: [
+        "Ordered", "PropOxi", "VoroPerco", "Coulomb", "VoroBV", "VoroInfo", "MergeSite"
+    ])
+
+
+@dataclass
+class ZeoTaskResult:
+    """单个任务结果"""
+    task_id: int
+    structure_name: str
+    cif_path: str
+    success: bool
+    output_files: List[str] = field(default_factory=list)
+    error_message: str = ""
+    duration: float = 0.0
+
+
+class ZeoExecutor:
+    """
+    Zeo++ 计算执行器
+    
+    使用 SLURM 作业数组高效调度大量 Voronoi 分析任务
+    """
+    
+    def __init__(self, config: ZeoConfig = None):
+        self.config = config or ZeoConfig()
+        self.progress_manager = None
+        self._stop_event = threading.Event()
+        
+    def run_batch(
+        self,
+        tasks: List[Dict],
+        output_dir: str = None,
+        desc: str = "Zeo++ 计算"
+    ) -> List[ZeoTaskResult]:
+        """
+        批量执行 Zeo++ 计算
+        
+        Args:
+            tasks: 任务列表，每个任务包含 cif_path, yaml_path, work_dir 等
+            output_dir: SLURM 日志输出目录
+            desc: 进度条描述
+            
+        Returns:
+            ZeoTaskResult 列表
+        """
+        if not tasks:
+            print("⚠️ 没有任务需要执行")
+            return []
+        
+        total = len(tasks)
+        
+        # 创建输出目录
+        if output_dir is None:
+            output_dir = os.path.join(os.getcwd(), "slurm_logs")
+        os.makedirs(output_dir, exist_ok=True)
+        
+        print(f"\n{'='*60}")
+        print(f"【Zeo++ 批量计算】")
+        print(f"{'='*60}")
+        print(f"  总任务数:     {total}")
+        print(f"  Conda环境:    {self.config.conda_env}")
+        print(f"  SLURM分区:    {self.config.partition}")
+        print(f"  最大并发:     {self.config.max_concurrent}")
+        print(f"  日志目录:     {output_dir}")
+        print(f"{'='*60}\n")
+        
+        # 保存任务列表到文件
+        tasks_file = os.path.join(output_dir, "tasks.json")
+        with open(tasks_file, 'w') as f:
+            json.dump(tasks, f, indent=2)
+        
+        # 生成并提交作业数组
+        if total <= self.config.max_array_size:
+            # 单个作业数组
+            return self._submit_array_job(tasks, output_dir, desc)
+        else:
+            # 分批提交多个作业数组
+            return self._submit_batched_arrays(tasks, output_dir, desc)
+    
+    def _submit_array_job(
+        self,
+        tasks: List[Dict],
+        output_dir: str,
+        desc: str
+    ) -> List[ZeoTaskResult]:
+        """提交单个作业数组"""
+        total = len(tasks)
+        
+        # 保存任务列表
+        tasks_file = os.path.join(output_dir, "tasks.json")
+        with open(tasks_file, 'w') as f:
+            json.dump(tasks, f, indent=2)
+        
+        # 生成作业脚本
+        script_content = self._generate_array_script(
+            tasks_file=tasks_file,
+            output_dir=output_dir,
+            array_range=f"0-{total-1}%{self.config.max_concurrent}"
+        )
+        
+        script_path = os.path.join(output_dir, "submit_array.sh")
+        with open(script_path, 'w') as f:
+            f.write(script_content)
+        os.chmod(script_path, 0o755)
+        
+        print(f"生成作业脚本: {script_path}")
+        
+        # 提交作业
+        result = subprocess.run(
+            ['sbatch', script_path],
+            capture_output=True,
+            text=True
+        )
+        
+        if result.returncode != 0:
+            print(f"❌ 作业提交失败: {result.stderr}")
+            return [ZeoTaskResult(
+                task_id=i,
+                structure_name=t.get('structure_name', ''),
+                cif_path=t.get('cif_path', ''),
+                success=False,
+                error_message=f"提交失败: {result.stderr}"
+            ) for i, t in enumerate(tasks)]
+        
+        # 提取作业 ID
+        job_id = result.stdout.strip().split()[-1]
+        print(f"✓ 作业已提交: {job_id}")
+        print(f"  作业数组范围: 0-{total-1}")
+        print(f"  最大并发: {self.config.max_concurrent}")
+        
+        # 监控作业进度
+        return self._monitor_array_job(job_id, tasks, output_dir, desc)
+    
+    def _submit_batched_arrays(
+        self,
+        tasks: List[Dict],
+        output_dir: str,
+        desc: str
+    ) -> List[ZeoTaskResult]:
+        """分批提交多个作业数组"""
+        total = len(tasks)
+        batch_size = self.config.max_array_size
+        num_batches = (total + batch_size - 1) // batch_size
+        
+        print(f"任务数超过作业数组限制，分 {num_batches} 批提交")
+        
+        all_results = []
+        
+        for batch_idx in range(num_batches):
+            start_idx = batch_idx * batch_size
+            end_idx = min(start_idx + batch_size, total)
+            batch_tasks = tasks[start_idx:end_idx]
+            
+            batch_output_dir = os.path.join(output_dir, f"batch_{batch_idx}")
+            os.makedirs(batch_output_dir, exist_ok=True)
+            
+            print(f"\n--- 批次 {batch_idx + 1}/{num_batches} ---")
+            print(f"任务范围: {start_idx} - {end_idx - 1}")
+            
+            batch_results = self._submit_array_job(
+                batch_tasks,
+                batch_output_dir,
+                f"{desc} (批次 {batch_idx + 1}/{num_batches})"
+            )
+            
+            # 调整任务 ID
+            for r in batch_results:
+                r.task_id += start_idx
+            
+            all_results.extend(batch_results)
+        
+        return all_results
+    
+    def _generate_array_script(
+        self,
+        tasks_file: str,
+        output_dir: str,
+        array_range: str
+    ) -> str:
+        """生成 SLURM 作业数组脚本"""
+        
+        # 获取项目根目录
+        project_root = os.getcwd()
+        
+        script = f"""#!/bin/bash
+#SBATCH --job-name=zeo_array
+#SBATCH --partition={self.config.partition}
+#SBATCH --array={array_range}
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task=1
+#SBATCH --mem={self.config.memory_per_task}
+#SBATCH --time={self.config.time_limit}
+#SBATCH --output={output_dir}/task_%a.out
+#SBATCH --error={output_dir}/task_%a.err
+
+# ============================================
+# Zeo++ Voronoi 分析 - 作业数组
+# ============================================
+
+echo "===== 任务信息 ====="
+echo "作业ID: $SLURM_JOB_ID"
+echo "数组任务ID: $SLURM_ARRAY_TASK_ID"
+echo "节点: $SLURM_NODELIST"
+echo "开始时间: $(date)"
+echo "===================="
+
+# ============ 环境初始化 ============
+# 加载 bashrc
+if [ -f ~/.bashrc ]; then
+    source ~/.bashrc
+fi
+
+# 初始化 Conda
+if [ -f ~/anaconda3/etc/profile.d/conda.sh ]; then
+    source ~/anaconda3/etc/profile.d/conda.sh
+elif [ -f /opt/anaconda3/etc/profile.d/conda.sh ]; then
+    source /opt/anaconda3/etc/profile.d/conda.sh
+fi
+
+# 激活 Zeo++ 环境
+conda activate {self.config.conda_env}
+
+echo ""
+echo "===== 环境检查 ====="
+echo "Conda环境: $CONDA_DEFAULT_ENV"
+echo "Python路径: $(which python)"
+echo "===================="
+echo ""
+
+# ============ 读取任务信息 ============
+TASKS_FILE="{tasks_file}"
+TASK_ID=$SLURM_ARRAY_TASK_ID
+
+# 使用 Python 解析任务
+TASK_INFO=$(python3 -c "
+import json
+with open('$TASKS_FILE', 'r') as f:
+    tasks = json.load(f)
+if $TASK_ID < len(tasks):
+    task = tasks[$TASK_ID]
+    print(task['work_dir'])
+    print(task['yaml_name'])
+else:
+    print('ERROR')
+")
+
+WORK_DIR=$(echo "$TASK_INFO" | sed -n '1p')
+YAML_NAME=$(echo "$TASK_INFO" | sed -n '2p')
+
+if [ "$WORK_DIR" == "ERROR" ]; then
+    echo "错误: 任务ID $TASK_ID 超出范围"
+    exit 1
+fi
+
+echo "工作目录: $WORK_DIR"
+echo "配置文件: $YAML_NAME"
+echo ""
+
+# ============ 执行计算 ============
+cd "$WORK_DIR"
+
+echo "开始 Voronoi 分析..."
+# 软链接已在工作目录下，直接使用相对路径
+# 将输出重定向到 log.txt 以便后续提取结果
+python analyze_voronoi_nodes.py *.cif -i "$YAML_NAME" > log.txt 2>&1
+
+EXIT_CODE=$?
+
+# 显示日志内容（用于调试）
+echo ""
+echo "===== 计算日志 ====="
+cat log.txt
+echo "===================="
+
+# ============ 完成 ============
+echo ""
+echo "===== 任务完成 ====="
+echo "结束时间: $(date)"
+echo "退出代码: $EXIT_CODE"
+
+# 写入状态文件
+if [ $EXIT_CODE -eq 0 ]; then
+    echo "SUCCESS" > "{output_dir}/status_$TASK_ID.txt"
+else
+    echo "FAILED" > "{output_dir}/status_$TASK_ID.txt"
+fi
+
+echo "===================="
+exit $EXIT_CODE
+"""
+        return script
+    
+    def _monitor_array_job(
+        self,
+        job_id: str,
+        tasks: List[Dict],
+        output_dir: str,
+        desc: str
+    ) -> List[ZeoTaskResult]:
+        """监控作业数组进度"""
+        total = len(tasks)
+        
+        self.progress_manager = ProgressManager(total, desc)
+        self.progress_manager.start()
+        
+        results = [None] * total
+        completed = set()
+        
+        print(f"\n监控作业进度 (每 {self.config.poll_interval} 秒检查一次)...")
+        print("按 Ctrl+C 可中断监控（作业将继续在后台运行）\n")
+        
+        try:
+            while len(completed) < total:
+                time.sleep(self.config.poll_interval)
+                
+                # 检查状态文件
+                for i in range(total):
+                    if i in completed:
+                        continue
+                    
+                    status_file = os.path.join(output_dir, f"status_{i}.txt")
+                    if os.path.exists(status_file):
+                        with open(status_file, 'r') as f:
+                            status = f.read().strip()
+                        
+                        task = tasks[i]
+                        success = (status == "SUCCESS")
+                        
+                        # 收集输出文件
+                        output_files = []
+                        if success:
+                            work_dir = task['work_dir']
+                            for f in os.listdir(work_dir):
+                                if f.endswith(('.cif', '.csv')) and f != task['cif_name']:
+                                    output_files.append(os.path.join(work_dir, f))
+                        
+                        results[i] = ZeoTaskResult(
+                            task_id=i,
+                            structure_name=task.get('structure_name', ''),
+                            cif_path=task.get('cif_path', ''),
+                            success=success,
+                            output_files=output_files
+                        )
+                        
+                        completed.add(i)
+                        self.progress_manager.update(success=success)
+                        self.progress_manager.display()
+                
+                # 检查作业是否还在运行
+                if not self._is_job_running(job_id) and len(completed) < total:
+                    # 作业已结束但有任务未完成
+                    print(f"\n⚠️ 作业已结束，但有 {total - len(completed)} 个任务未完成")
+                    break
+                    
+        except KeyboardInterrupt:
+            print("\n\n⚠️ 监控已中断，作业将继续在后台运行")
+            print(f"   可使用 'squeue -j {job_id}' 查看作业状态")
+            print(f"   可使用 'scancel {job_id}' 取消作业")
+        
+        self.progress_manager.finish()
+        
+        # 填充未完成的任务
+        for i in range(total):
+            if results[i] is None:
+                task = tasks[i]
+                results[i] = ZeoTaskResult(
+                    task_id=i,
+                    structure_name=task.get('structure_name', ''),
+                    cif_path=task.get('cif_path', ''),
+                    success=False,
+                    error_message="任务未完成或状态未知"
+                )
+        
+        return results
+    
+    def _is_job_running(self, job_id: str) -> bool:
+        """检查作业是否还在运行"""
+        try:
+            result = subprocess.run(
+                ['squeue', '-j', job_id, '-h'],
+                capture_output=True,
+                text=True,
+                timeout=10
+            )
+            return bool(result.stdout.strip())
+        except Exception:
+            return False
+    
+    def print_results_summary(self, results: List[ZeoTaskResult]):
+        """打印结果摘要"""
+        total = len(results)
+        success = sum(1 for r in results if r.success)
+        failed = total - success
+        
+        print("\n" + "=" * 60)
+        print("【计算结果摘要】")
+        print("=" * 60)
+        print(f"  总任务数:   {total}")
+        print(f"  成功:       {success} ({100*success/total:.1f}%)")
+        print(f"  失败:       {failed} ({100*failed/total:.1f}%)")
+        
+        if failed > 0 and failed <= 10:
+            print("\n  失败的任务:")
+            for r in results:
+                if not r.success:
+                    print(f"    - {r.structure_name}: {r.error_message}")
+        elif failed > 10:
+            print(f"\n  失败任务过多，请检查日志文件")
+        
+        print("=" * 60)

src/core/__init__.py

@@ -0,0 +1,18 @@
+"""
+核心模块：调度器、执行器和进度管理
+"""
+from .scheduler import ParallelScheduler, ResourceConfig, ExecutionMode as SchedulerMode
+from .executor import TaskExecutor, ExecutorConfig, ExecutionMode, TaskResult, create_executor
+from .progress import ProgressManager
+
+__all__ = [
+    'ParallelScheduler',
+    'ResourceConfig',
+    'SchedulerMode',
+    'TaskExecutor',
+    'ExecutorConfig',
+    'ExecutionMode',
+    'TaskResult',
+    'create_executor',
+    'ProgressManager',
+]

src/core/executor.py

@@ -0,0 +1,431 @@
+"""
+任务执行器：支持本地执行和 SLURM 直接提交
+不生成脚本文件，直接在 Python 中管理任务
+"""
+import os
+import subprocess
+import time
+import json
+from typing import List, Callable, Any, Optional, Dict, Tuple
+from multiprocessing import Pool, cpu_count
+from dataclasses import dataclass, field
+from enum import Enum
+from concurrent.futures import ThreadPoolExecutor, as_completed
+import threading
+
+from .progress import ProgressManager
+
+
+class ExecutionMode(Enum):
+    """执行模式"""
+    LOCAL = "local"           # 本地多进程
+    SLURM_DIRECT = "slurm"    # SLURM 直接提交（不生成脚本）
+
+
+@dataclass
+class ExecutorConfig:
+    """执行器配置"""
+    mode: ExecutionMode = ExecutionMode.LOCAL
+    max_workers: int = 4
+    conda_env: str = "/cluster/home/koko125/anaconda3/envs/screen"
+    partition: str = "cpu"
+    time_limit: str = "7-00:00:00"
+    memory_per_task: str = "4G"
+    
+    # SLURM 相关
+    poll_interval: float = 2.0  # 轮询间隔（秒）
+    max_concurrent_jobs: int = 50  # 最大并发作业数
+
+
+@dataclass
+class TaskResult:
+    """任务结果"""
+    task_id: Any
+    success: bool
+    result: Any = None
+    error: str = None
+    duration: float = 0.0
+
+
+class TaskExecutor:
+    """
+    任务执行器
+    
+    支持两种模式：
+    1. LOCAL: 本地多进程执行
+    2. SLURM_DIRECT: 直接提交 SLURM 作业，实时监控进度
+    """
+    
+    def __init__(self, config: ExecutorConfig = None):
+        self.config = config or ExecutorConfig()
+        self.progress_manager = None
+        self._stop_event = threading.Event()
+        
+    @staticmethod
+    def detect_environment() -> Dict[str, Any]:
+        """检测运行环境"""
+        env_info = {
+            'hostname': os.uname().nodename,
+            'total_cores': cpu_count(),
+            'has_slurm': False,
+            'slurm_partitions': [],
+            'conda_env': os.environ.get('CONDA_PREFIX', ''),
+        }
+        
+        # 检测 SLURM
+        try:
+            result = subprocess.run(
+                ['sinfo', '-h', '-o', '%P %a %c %D'],
+                capture_output=True, text=True, timeout=5
+            )
+            if result.returncode == 0:
+                env_info['has_slurm'] = True
+                lines = result.stdout.strip().split('\n')
+                for line in lines:
+                    parts = line.split()
+                    if len(parts) >= 4:
+                        partition = parts[0].rstrip('*')
+                        avail = parts[1]
+                        if avail == 'up':
+                            env_info['slurm_partitions'].append(partition)
+        except Exception:
+            pass
+            
+        return env_info
+    
+    def run(
+        self,
+        tasks: List[Any],
+        worker_func: Callable,
+        desc: str = "Processing"
+    ) -> List[TaskResult]:
+        """
+        执行任务
+        
+        Args:
+            tasks: 任务列表
+            worker_func: 工作函数，接收单个任务，返回结果
+            desc: 进度条描述
+            
+        Returns:
+            TaskResult 列表
+        """
+        if self.config.mode == ExecutionMode.LOCAL:
+            return self._run_local(tasks, worker_func, desc)
+        elif self.config.mode == ExecutionMode.SLURM_DIRECT:
+            return self._run_slurm_direct(tasks, worker_func, desc)
+        else:
+            raise ValueError(f"不支持的执行模式: {self.config.mode}")
+    
+    def _run_local(
+        self,
+        tasks: List[Any],
+        worker_func: Callable,
+        desc: str
+    ) -> List[TaskResult]:
+        """本地多进程执行"""
+        total = len(tasks)
+        num_workers = min(self.config.max_workers, total)
+        
+        print(f"\n{'='*60}")
+        print(f"本地执行配置:")
+        print(f"  总任务数:   {total}")
+        print(f"  Worker数:   {num_workers}")
+        print(f"{'='*60}\n")
+        
+        self.progress_manager = ProgressManager(total, desc)
+        self.progress_manager.start()
+        
+        results = []
+        
+        if num_workers == 1:
+            # 单进程执行
+            for i, task in enumerate(tasks):
+                start_time = time.time()
+                try:
+                    result = worker_func(task)
+                    duration = time.time() - start_time
+                    results.append(TaskResult(
+                        task_id=i,
+                        success=True,
+                        result=result,
+                        duration=duration
+                    ))
+                    self.progress_manager.update(success=True)
+                except Exception as e:
+                    duration = time.time() - start_time
+                    results.append(TaskResult(
+                        task_id=i,
+                        success=False,
+                        error=str(e),
+                        duration=duration
+                    ))
+                    self.progress_manager.update(success=False)
+                self.progress_manager.display()
+        else:
+            # 多进程执行
+            with Pool(processes=num_workers) as pool:
+                for i, result in enumerate(pool.imap_unordered(worker_func, tasks)):
+                    if result is not None:
+                        results.append(TaskResult(
+                            task_id=i,
+                            success=True,
+                            result=result
+                        ))
+                        self.progress_manager.update(success=True)
+                    else:
+                        results.append(TaskResult(
+                            task_id=i,
+                            success=False,
+                            error="Worker returned None"
+                        ))
+                        self.progress_manager.update(success=False)
+                    self.progress_manager.display()
+        
+        self.progress_manager.finish()
+        return results
+    
+    def _run_slurm_direct(
+        self,
+        tasks: List[Any],
+        worker_func: Callable,
+        desc: str
+    ) -> List[TaskResult]:
+        """
+        SLURM 直接提交模式
+        
+        注意：对于数据库分析这类快速任务，建议使用本地多进程模式
+        SLURM 模式更适合耗时的计算任务（如 Zeo++ 分析）
+        
+        这里回退到本地模式，因为 srun 在登录节点直接调用效率不高
+        """
+        print("\n⚠️ 注意：数据库分析阶段自动使用本地多进程模式")
+        print("   SLURM 模式将在后续耗时计算步骤中使用")
+        
+        # 回退到本地模式
+        return self._run_local(tasks, worker_func, desc)
+
+
+class SlurmJobManager:
+    """
+    SLURM 作业管理器
+    
+    用于批量提交和监控 SLURM 作业
+    """
+    
+    def __init__(self, config: ExecutorConfig):
+        self.config = config
+        self.active_jobs = {}  # job_id -> task_info
+        
+    def submit_batch(
+        self,
+        tasks: List[Tuple[str, str, set]],  # (file_path, target_cation, target_anions)
+        output_dir: str,
+        desc: str = "Processing"
+    ) -> List[TaskResult]:
+        """
+        批量提交任务到 SLURM
+        
+        使用 sbatch --wrap 直接提交，不生成脚本文件
+        """
+        total = len(tasks)
+        os.makedirs(output_dir, exist_ok=True)
+        
+        print(f"\n{'='*60}")
+        print(f"SLURM 批量提交:")
+        print(f"  总任务数:     {total}")
+        print(f"  输出目录:     {output_dir}")
+        print(f"  Conda环境:    {self.config.conda_env}")
+        print(f"{'='*60}\n")
+        
+        progress = ProgressManager(total, desc)
+        progress.start()
+        
+        results = []
+        job_ids = []
+        
+        # 提交所有任务
+        for i, task in enumerate(tasks):
+            file_path, target_cation, target_anions = task
+            
+            # 构建 Python 命令
+            anions_str = ','.join(target_anions)
+            python_cmd = (
+                f"python -c \""
+                f"import sys; sys.path.insert(0, '{os.getcwd()}'); "
+                f"from src.analysis.worker import analyze_single_file; "
+                f"result = analyze_single_file(('{file_path}', '{target_cation}', set('{anions_str}'.split(',')))); "
+                f"print('SUCCESS' if result and result.is_valid else 'FAILED')"
+                f"\""
+            )
+            
+            # 构建完整的 bash 命令
+            bash_cmd = (
+                f"source {os.path.dirname(self.config.conda_env)}/../../etc/profile.d/conda.sh && "
+                f"conda activate {self.config.conda_env} && "
+                f"{python_cmd}"
+            )
+            
+            # 使用 sbatch --wrap 提交
+            sbatch_cmd = [
+                'sbatch',
+                '--partition', self.config.partition,
+                '--ntasks', '1',
+                '--cpus-per-task', '1',
+                '--mem', self.config.memory_per_task,
+                '--time', '01:00:00',
+                '--output', os.path.join(output_dir, f'task_{i}.out'),
+                '--error', os.path.join(output_dir, f'task_{i}.err'),
+                '--wrap', bash_cmd
+            ]
+            
+            try:
+                result = subprocess.run(
+                    sbatch_cmd,
+                    capture_output=True,
+                    text=True
+                )
+                
+                if result.returncode == 0:
+                    # 提取 job_id
+                    job_id = result.stdout.strip().split()[-1]
+                    job_ids.append((i, job_id, file_path))
+                    self.active_jobs[job_id] = {
+                        'task_index': i,
+                        'file_path': file_path,
+                        'status': 'PENDING'
+                    }
+                else:
+                    results.append(TaskResult(
+                        task_id=i,
+                        success=False,
+                        error=f"提交失败: {result.stderr}"
+                    ))
+                    progress.update(success=False)
+                    progress.display()
+                    
+            except Exception as e:
+                results.append(TaskResult(
+                    task_id=i,
+                    success=False,
+                    error=str(e)
+                ))
+                progress.update(success=False)
+                progress.display()
+        
+        print(f"\n已提交 {len(job_ids)} 个作业，等待完成...")
+        
+        # 监控作业状态
+        while self.active_jobs:
+            time.sleep(self.config.poll_interval)
+            
+            # 检查作业状态
+            completed_jobs = self._check_job_status()
+            
+            for job_id, status in completed_jobs:
+                job_info = self.active_jobs.pop(job_id, None)
+                if job_info:
+                    task_idx = job_info['task_index']
+                    
+                    if status == 'COMPLETED':
+                        # 检查输出文件
+                        out_file = os.path.join(output_dir, f'task_{task_idx}.out')
+                        success = False
+                        if os.path.exists(out_file):
+                            with open(out_file, 'r') as f:
+                                content = f.read()
+                                success = 'SUCCESS' in content
+                        
+                        results.append(TaskResult(
+                            task_id=task_idx,
+                            success=success,
+                            result=job_info['file_path']
+                        ))
+                        progress.update(success=success)
+                    else:
+                        # 作业失败
+                        err_file = os.path.join(output_dir, f'task_{task_idx}.err')
+                        error_msg = status
+                        if os.path.exists(err_file):
+                            with open(err_file, 'r') as f:
+                                error_msg = f.read()[:500]  # 只取前500字符
+                        
+                        results.append(TaskResult(
+                            task_id=task_idx,
+                            success=False,
+                            error=error_msg
+                        ))
+                        progress.update(success=False)
+                    
+                    progress.display()
+        
+        progress.finish()
+        return results
+    
+    def _check_job_status(self) -> List[Tuple[str, str]]:
+        """检查作业状态，返回已完成的作业列表"""
+        if not self.active_jobs:
+            return []
+        
+        job_ids = list(self.active_jobs.keys())
+        
+        try:
+            result = subprocess.run(
+                ['sacct', '-j', ','.join(job_ids), '--format=JobID,State', '--noheader', '--parsable2'],
+                capture_output=True,
+                text=True,
+                timeout=30
+            )
+            
+            completed = []
+            if result.returncode == 0:
+                for line in result.stdout.strip().split('\n'):
+                    if line:
+                        parts = line.split('|')
+                        if len(parts) >= 2:
+                            job_id = parts[0].split('.')[0]  # 去掉 .batch 后缀
+                            status = parts[1]
+                            
+                            if job_id in self.active_jobs:
+                                if status in ['COMPLETED', 'FAILED', 'CANCELLED', 'TIMEOUT', 'NODE_FAIL']:
+                                    completed.append((job_id, status))
+            
+            return completed
+            
+        except Exception:
+            return []
+
+
+def create_executor(
+    mode: str = "local",
+    max_workers: int = None,
+    conda_env: str = None,
+    **kwargs
+) -> TaskExecutor:
+    """
+    创建任务执行器的便捷函数
+    
+    Args:
+        mode: "local" 或 "slurm"
+        max_workers: 最大工作进程数
+        conda_env: Conda 环境路径
+        **kwargs: 其他配置参数
+    """
+    env = TaskExecutor.detect_environment()
+    
+    if max_workers is None:
+        max_workers = min(env['total_cores'], 32)
+    
+    if conda_env is None:
+        conda_env = env.get('conda_env') or "/cluster/home/koko125/anaconda3/envs/screen"
+    
+    exec_mode = ExecutionMode.SLURM_DIRECT if mode.lower() == "slurm" else ExecutionMode.LOCAL
+    
+    config = ExecutorConfig(
+        mode=exec_mode,
+        max_workers=max_workers,
+        conda_env=conda_env,
+        **kwargs
+    )
+    
+    return TaskExecutor(config)

src/core/progress.py

@@ -0,0 +1,115 @@
+"""
+进度管理器：支持多进程的实时进度显示
+"""
+import os
+import sys
+import time
+from multiprocessing import Manager, Value
+from typing import Optional
+from datetime import datetime, timedelta
+
+
+class ProgressManager:
+    """多进程安全的进度管理器"""
+
+    def __init__(self, total: int, desc: str = "Processing"):
+        self.total = total
+        self.desc = desc
+        self.manager = Manager()
+        self.completed = self.manager.Value('i', 0)
+        self.failed = self.manager.Value('i', 0)
+        self.start_time = None
+        self._lock = self.manager.Lock()
+
+    def start(self):
+        """开始计时"""
+        self.start_time = time.time()
+
+    def update(self, success: bool = True):
+        """更新进度（进程安全）"""
+        with self._lock:
+            if success:
+                self.completed.value += 1
+            else:
+                self.failed.value += 1
+
+    def get_progress(self) -> dict:
+        """获取当前进度"""
+        completed = self.completed.value
+        failed = self.failed.value
+        total_done = completed + failed
+
+        elapsed = time.time() - self.start_time if self.start_time else 0
+
+        if total_done > 0:
+            speed = total_done / elapsed  # items/sec
+            remaining = (self.total - total_done) / speed if speed > 0 else 0
+        else:
+            speed = 0
+            remaining = 0
+
+        return {
+            'total': self.total,
+            'completed': completed,
+            'failed': failed,
+            'total_done': total_done,
+            'percent': total_done / self.total * 100 if self.total > 0 else 0,
+            'elapsed': elapsed,
+            'remaining': remaining,
+            'speed': speed
+        }
+
+    def display(self):
+        """显示进度条"""
+        p = self.get_progress()
+
+        # 进度条
+        bar_width = 40
+        filled = int(bar_width * p['total_done'] / p['total']) if p['total'] > 0 else 0
+        bar = '█' * filled + '░' * (bar_width - filled)
+
+        # 时间格式化
+        elapsed_str = str(timedelta(seconds=int(p['elapsed'])))
+        remaining_str = str(timedelta(seconds=int(p['remaining'])))
+
+        # 构建显示字符串
+        status = (
+            f"\r{self.desc}: |{bar}| "
+            f"{p['total_done']}/{p['total']} ({p['percent']:.1f}%) "
+            f"[{elapsed_str}<{remaining_str}, {p['speed']:.1f}it/s] "
+            f"✓{p['completed']} ✗{p['failed']}"
+        )
+
+        sys.stdout.write(status)
+        sys.stdout.flush()
+
+    def finish(self):
+        """完成显示"""
+        self.display()
+        print()  # 换行
+
+
+class ProgressReporter:
+    """进度报告器（用于后台监控）"""
+
+    def __init__(self, progress_file: str = ".progress"):
+        self.progress_file = progress_file
+
+    def save(self, progress: dict):
+        """保存进度到文件"""
+        import json
+        with open(self.progress_file, 'w') as f:
+            json.dump(progress, f)
+
+    def load(self) -> Optional[dict]:
+        """从文件加载进度"""
+        import json
+        if os.path.exists(self.progress_file):
+            with open(self.progress_file, 'r') as f:
+                return json.load(f)
+        return None
+
+    def cleanup(self):
+        """清理进度文件"""
+        if os.path.exists(self.progress_file):
+            os.remove(self.progress_file)

src/core/scheduler.py

@@ -0,0 +1,291 @@
+"""
+并行调度器：支持本地多进程和SLURM集群调度
+"""
+import os
+import subprocess
+import tempfile
+from typing import List, Callable, Any, Optional, Dict
+from multiprocessing import Pool, cpu_count
+from dataclasses import dataclass
+from enum import Enum
+import math
+
+from .progress import ProgressManager
+
+
+class ExecutionMode(Enum):
+    """执行模式"""
+    LOCAL_SINGLE = "local_single"
+    LOCAL_MULTI = "local_multi"
+    SLURM_SINGLE = "slurm_single"
+    SLURM_ARRAY = "slurm_array"
+
+
+@dataclass
+class ResourceConfig:
+    """资源配置"""
+    max_cores: int = 4
+    cores_per_worker: int = 1
+    memory_per_core: str = "4G"
+    partition: str = "cpu"
+    time_limit: str = "7-00:00:00"
+    conda_env_path: str = "~/anaconda3/envs/screen"  # 新增：Conda环境路径
+
+    @property
+    def num_workers(self) -> int:
+        return max(1, self.max_cores // self.cores_per_worker)
+
+
+class ParallelScheduler:
+    """并行调度器"""
+
+    COMPLEXITY_CORES = {
+        'low': 1,
+        'medium': 2,
+        'high': 4,
+    }
+
+    def __init__(self, resource_config: ResourceConfig = None):
+        self.config = resource_config or ResourceConfig()
+        self.progress_manager = None
+
+    @staticmethod
+    def detect_environment() -> Dict[str, Any]:
+        """检测运行环境"""
+        env_info = {
+            'hostname': os.uname().nodename,
+            'total_cores': cpu_count(),
+            'has_slurm': False,
+            'slurm_partitions': [],
+            'available_nodes': 0,
+            'conda_prefix': os.environ.get('CONDA_PREFIX', ''),
+        }
+
+        # 检测SLURM
+        try:
+            result = subprocess.run(
+                ['sinfo', '-h', '-o', '%P %a %c %D'],
+                capture_output=True, text=True, timeout=5
+            )
+            if result.returncode == 0:
+                env_info['has_slurm'] = True
+                lines = result.stdout.strip().split('\n')
+                for line in lines:
+                    parts = line.split()
+                    if len(parts) >= 4:
+                        partition = parts[0].rstrip('*')
+                        avail = parts[1]
+                        cores = int(parts[2])
+                        nodes = int(parts[3])
+                        if avail == 'up':
+                            env_info['slurm_partitions'].append({
+                                'name': partition,
+                                'cores_per_node': cores,
+                                'nodes': nodes
+                            })
+                            env_info['available_nodes'] += nodes
+        except Exception:
+            pass
+
+        return env_info
+
+    @staticmethod
+    def recommend_config(
+        num_tasks: int,
+        task_complexity: str = 'medium',
+        max_cores: int = None,
+        conda_env_path: str = None
+    ) -> ResourceConfig:
+        """根据任务量和复杂度推荐配置"""
+
+        env = ParallelScheduler.detect_environment()
+
+        if max_cores is None:
+            max_cores = min(env['total_cores'], 32)
+
+        cores_per_worker = ParallelScheduler.COMPLEXITY_CORES.get(task_complexity, 2)
+        max_workers = max_cores // cores_per_worker
+        optimal_workers = min(num_tasks, max_workers)
+        actual_cores = optimal_workers * cores_per_worker
+
+        # 自动检测Conda环境路径
+        if conda_env_path is None:
+            conda_env_path = env.get('conda_prefix', '~/anaconda3/envs/screen')
+
+        config = ResourceConfig(
+            max_cores=actual_cores,
+            cores_per_worker=cores_per_worker,
+            conda_env_path=conda_env_path,
+        )
+
+        return config
+
+    def run_local(
+        self,
+        tasks: List[Any],
+        worker_func: Callable,
+        desc: str = "Processing"
+    ) -> List[Any]:
+        """本地多进程执行"""
+
+        num_workers = self.config.num_workers
+        total = len(tasks)
+
+        print(f"\n{'='*60}")
+        print(f"并行配置:")
+        print(f"  总任务数:     {total}")
+        print(f"  Worker数:     {num_workers}")
+        print(f"  每Worker核数: {self.config.cores_per_worker}")
+        print(f"  总使用核数:   {self.config.max_cores}")
+        print(f"{'='*60}\n")
+
+        self.progress_manager = ProgressManager(total, desc)
+        self.progress_manager.start()
+
+        results = []
+
+        if num_workers == 1:
+            for task in tasks:
+                try:
+                    result = worker_func(task)
+                    results.append(result)
+                    self.progress_manager.update(success=True)
+                except Exception as e:
+                    results.append(None)
+                    self.progress_manager.update(success=False)
+                self.progress_manager.display()
+        else:
+            with Pool(processes=num_workers) as pool:
+                for result in pool.imap_unordered(worker_func, tasks, chunksize=10):
+                    if result is not None:
+                        results.append(result)
+                        self.progress_manager.update(success=True)
+                    else:
+                        self.progress_manager.update(success=False)
+                    self.progress_manager.display()
+
+        self.progress_manager.finish()
+
+        return results
+
+    def generate_slurm_script(
+        self,
+        tasks_file: str,
+        worker_script: str,
+        output_dir: str,
+        job_name: str = "analysis"
+    ) -> str:
+        """生成SLURM作业脚本（修复版）"""
+
+        # 获取当前工作目录的绝对路径
+        submit_dir = os.getcwd()
+
+        # 转换为绝对路径
+        abs_tasks_file = os.path.abspath(tasks_file)
+        abs_worker_script = os.path.abspath(worker_script)
+        abs_output_dir = os.path.abspath(output_dir)
+
+        script = f"""#!/bin/bash
+#SBATCH --job-name={job_name}
+#SBATCH --partition={self.config.partition}
+#SBATCH --nodes=1
+#SBATCH --ntasks=1
+#SBATCH --cpus-per-task={self.config.max_cores}
+#SBATCH --mem-per-cpu={self.config.memory_per_core}
+#SBATCH --time={self.config.time_limit}
+#SBATCH --output={abs_output_dir}/slurm_%j.log
+#SBATCH --error={abs_output_dir}/slurm_%j.err
+
+# ============================================
+# SLURM作业脚本 - 自动生成
+# ============================================
+
+echo "===== 作业信息 ====="
+echo "作业ID: $SLURM_JOB_ID"
+echo "节点: $SLURM_NODELIST"
+echo "CPU数: $SLURM_CPUS_PER_TASK"
+echo "开始时间: $(date)"
+echo "===================="
+
+# ============ 环境初始化 ============
+# 关键：确保bashrc被加载
+if [ -f ~/.bashrc ]; then
+    source ~/.bashrc
+fi
+
+# 初始化Conda
+if [ -f ~/anaconda3/etc/profile.d/conda.sh ]; then
+    source ~/anaconda3/etc/profile.d/conda.sh
+elif [ -f /opt/anaconda3/etc/profile.d/conda.sh ]; then
+    source /opt/anaconda3/etc/profile.d/conda.sh
+else
+    echo "错误: 找不到conda.sh"
+    exit 1
+fi
+
+# 激活环境 (使用完整路径)
+conda activate {self.config.conda_env_path}
+
+# 验证环境
+echo ""
+echo "===== 环境检查 ====="
+echo "Conda环境: $CONDA_DEFAULT_ENV"
+echo "Python路径: $(which python)"
+echo "Python版本: $(python --version 2>&1)"
+python -c "import pymatgen; print(f'pymatgen版本: {{pymatgen.__version__}}')" 2>/dev/null || echo "警告: pymatgen未安装"
+echo "===================="
+echo ""
+
+# 设置工作目录
+cd {submit_dir}
+export PYTHONPATH={submit_dir}:$PYTHONPATH
+
+echo "工作目录: $(pwd)"
+echo "PYTHONPATH: $PYTHONPATH"
+echo ""
+
+# ============ 运行分析 ============
+echo "开始执行分析任务..."
+python {abs_worker_script} \\
+    --tasks-file {abs_tasks_file} \\
+    --output-dir {abs_output_dir} \\
+    --num-workers {self.config.num_workers}
+
+EXIT_CODE=$?
+
+# ============ 完成 ============
+echo ""
+echo "===== 作业完成 ====="
+echo "结束时间: $(date)"
+echo "退出代码: $EXIT_CODE"
+echo "===================="
+
+exit $EXIT_CODE
+"""
+        return script
+
+    def submit_slurm_job(self, script_content: str, script_path: str = None) -> str:
+        """提交SLURM作业"""
+
+        if script_path is None:
+            fd, script_path = tempfile.mkstemp(suffix='.sh')
+            os.close(fd)
+
+        with open(script_path, 'w') as f:
+            f.write(script_content)
+
+        os.chmod(script_path, 0o755)
+
+        # 打印脚本内容用于调试
+        print(f"\n生成的SLURM脚本保存到: {script_path}")
+
+        result = subprocess.run(
+            ['sbatch', script_path],
+            capture_output=True, text=True
+        )
+
+        if result.returncode == 0:
+            job_id = result.stdout.strip().split()[-1]
+            return job_id
+        else:
+            raise RuntimeError(f"SLURM提交失败:\nstdout: {result.stdout}\nstderr: {result.stderr}")

src/preprocessing/processor.py

@@ -0,0 +1,562 @@
+"""
+结构预处理器：扩胞和添加化合价
+"""
+import os
+import re
+import yaml
+from typing import List, Dict, Optional, Tuple
+from dataclasses import dataclass, field
+from pymatgen.core.structure import Structure
+from pymatgen.core.periodic_table import Specie
+from pymatgen.core import Lattice, Species, PeriodicSite
+from collections import defaultdict
+from fractions import Fraction
+from functools import reduce
+import math
+import random
+import spglib
+import numpy as np
+
+
+@dataclass
+class ProcessingResult:
+    """处理结果"""
+    input_file: str
+    output_files: List[str] = field(default_factory=list)
+    success: bool = False
+    needs_expansion: bool = False
+    expansion_factor: int = 1
+    error_message: str = ""
+
+
+class StructureProcessor:
+    """结构预处理器"""
+    
+    # 默认化合价配置
+    DEFAULT_VALENCE_PATH = os.path.join(
+        os.path.dirname(__file__), '..', '..', 'tool', 'valence_states.yaml'
+    )
+    
+    def __init__(
+        self,
+        valence_yaml_path: str = None,
+        calculate_type: str = 'low',
+        max_expansion_factor: int = 64,
+        keep_number: int = 3,
+        target_cation: str = "Li"
+    ):
+        """
+        初始化处理器
+        
+        Args:
+            valence_yaml_path: 化合价配置文件路径
+            calculate_type: 扩胞计算精度 ('high', 'normal', 'low', 'very_low')
+            max_expansion_factor: 最大扩胞因子
+            keep_number: 保留的扩胞结构数量
+            target_cation: 目标阳离子
+        """
+        self.valence_yaml_path = valence_yaml_path or self.DEFAULT_VALENCE_PATH
+        self.calculate_type = calculate_type
+        self.max_expansion_factor = max_expansion_factor
+        self.keep_number = keep_number
+        self.target_cation = target_cation
+        self.explict_element = [target_cation, f"{target_cation}+"]
+        
+        # 加载化合价配置
+        self.valences = self._load_valences()
+    
+    def _load_valences(self) -> Dict[str, int]:
+        """加载化合价配置"""
+        if os.path.exists(self.valence_yaml_path):
+            with open(self.valence_yaml_path, 'r') as f:
+                return yaml.safe_load(f)
+        return {}
+    
+    def process_file(
+        self,
+        input_path: str,
+        output_dir: str,
+        needs_expansion: bool = False
+    ) -> ProcessingResult:
+        """
+        处理单个CIF文件
+        
+        Args:
+            input_path: 输入文件路径
+            output_dir: 输出目录
+            needs_expansion: 是否需要扩胞
+            
+        Returns:
+            ProcessingResult: 处理结果
+        """
+        result = ProcessingResult(input_file=input_path)
+        
+        try:
+            # 读取结构
+            structure = Structure.from_file(input_path)
+            base_name = os.path.splitext(os.path.basename(input_path))[0]
+            
+            # 检查是否需要扩胞
+            occupation_list = self._process_cif_file(structure)
+            
+            if occupation_list and needs_expansion:
+                # 需要扩胞处理
+                result.needs_expansion = True
+                output_files = self._expand_and_save(
+                    structure, occupation_list, base_name, output_dir
+                )
+                result.output_files = output_files
+                result.expansion_factor = occupation_list[0].get('denominator', 1) if occupation_list else 1
+            else:
+                # 不需要扩胞，直接添加化合价
+                output_path = os.path.join(output_dir, f"{base_name}.cif")
+                self._add_oxidation_states(structure)
+                structure.to(filename=output_path)
+                result.output_files = [output_path]
+            
+            result.success = True
+            
+        except Exception as e:
+            result.success = False
+            result.error_message = str(e)
+        
+        return result
+    
+    def _process_cif_file(self, structure: Structure) -> List[Dict]:
+        """
+        统计结构中各原子的occupation情况
+        """
+        occupation_dict = defaultdict(list)
+        split_dict = {}
+        
+        for i, site in enumerate(structure):
+            occu = self._get_occu(site.species_string)
+            
+            if occu != 1.0:
+                if site.species.chemical_system not in self.explict_element:
+                    occupation_dict[occu].append(i + 1)
+                    
+                    # 提取元素名称列表
+                    elements = []
+                    if ':' in site.species_string:
+                        parts = site.species_string.split(',')
+                        for part in parts:
+                            element_with_valence = part.strip().split(':')[0].strip()
+                            element_match = re.match(r'([A-Z][a-z]?)', element_with_valence)
+                            if element_match:
+                                elements.append(element_match.group(1))
+                    else:
+                        element_match = re.match(r'([A-Z][a-z]?)', site.species_string)
+                        if element_match:
+                            elements = [element_match.group(1)]
+                    
+                    split_dict[occu] = elements
+        
+        # 转换为列表格式
+        occupation_list = [
+            {
+                "occupation": occu,
+                "atom_serial": serials,
+                "numerator": None,
+                "denominator": None,
+                "split": split_dict.get(occu, [])
+            }
+            for occu, serials in occupation_dict.items()
+        ]
+        
+        return occupation_list
+    
+    def _get_occu(self, s_str: str) -> float:
+        """从物种字符串获取占据率"""
+        if not s_str.strip():
+            return 1.0
+        
+        pattern = r'([A-Za-z0-9+-]+):([0-9.]+)'
+        matches = re.findall(pattern, s_str)
+        
+        for species, occu in matches:
+            if species not in self.explict_element:
+                try:
+                    return float(occu)
+                except ValueError:
+                    continue
+        
+        return 1.0
+    
+    def _calculate_expansion_factor(self, occupation_list: List[Dict]) -> Tuple[int, List[Dict]]:
+        """计算扩胞因子"""
+        if not occupation_list:
+            return 1, []
+        
+        precision_limits = {
+            'high': None,
+            'normal': 100,
+            'low': 10,
+            'very_low': 5
+        }
+        
+        limit = precision_limits.get(self.calculate_type)
+        
+        for entry in occupation_list:
+            occu = entry["occupation"]
+            if limit:
+                fraction = Fraction(occu).limit_denominator(limit)
+            else:
+                fraction = Fraction(occu).limit_denominator()
+            
+            entry["numerator"] = fraction.numerator
+            entry["denominator"] = fraction.denominator
+        
+        # 计算最小公倍数
+        denominators = [entry["denominator"] for entry in occupation_list]
+        lcm = reduce(lambda a, b: a * b // math.gcd(a, b), denominators, 1)
+        
+        # 统一分母
+        for entry in occupation_list:
+            denominator = entry["denominator"]
+            entry["numerator"] = entry["numerator"] * (lcm // denominator)
+            entry["denominator"] = lcm
+        
+        return lcm, occupation_list
+    
+    def _expand_and_save(
+        self,
+        structure: Structure,
+        occupation_list: List[Dict],
+        base_name: str,
+        output_dir: str
+    ) -> List[str]:
+        """扩胞并保存"""
+        lcm, oc_list = self._calculate_expansion_factor(occupation_list)
+        
+        if lcm > self.max_expansion_factor:
+            raise ValueError(f"扩胞因子 {lcm} 超过最大限制 {self.max_expansion_factor}")
+        
+        # 获取扩胞策略
+        strategies = self._strategy_divide(structure, lcm)
+        
+        if not strategies:
+            raise ValueError("无法找到合适的扩胞策略")
+        
+        # 生成结构列表
+        st_list = self._generate_structure_list(structure, oc_list)
+        
+        output_files = []
+        keep_number = min(self.keep_number, len(strategies))
+        
+        for index in range(keep_number):
+            merged = self._merge_structures(st_list, strategies[index])
+            
+            # 添加化合价
+            self._add_oxidation_states(merged)
+            
+            # 当只保存1个时不加后缀
+            if keep_number == 1:
+                output_filename = f"{base_name}.cif"
+            else:
+                suffix = "x{}y{}z{}".format(
+                    strategies[index]["x"],
+                    strategies[index]["y"],
+                    strategies[index]["z"]
+                )
+                output_filename = f"{base_name}-{suffix}.cif"
+            
+            output_path = os.path.join(output_dir, output_filename)
+            merged.to(filename=output_path, fmt="cif")
+            output_files.append(output_path)
+        
+        return output_files
+    
+    def _add_oxidation_states(self, structure: Structure):
+        """添加化合价"""
+        # 检查是否已有化合价
+        has_oxidation = all(
+            all(isinstance(sp, Specie) for sp in site.species.keys())
+            for site in structure.sites
+        )
+        
+        if not has_oxidation and self.valences:
+            structure.add_oxidation_state_by_element(self.valences)
+    
+    def _strategy_divide(self, structure: Structure, total: int) -> List[Dict]:
+        """根据晶体类型确定扩胞策略"""
+        try:
+            space_group_info = structure.get_space_group_info()
+            space_group_symbol = space_group_info[0]
+            
+            # 获取空间群类型
+            all_spacegroup_symbols = [spglib.get_spacegroup_type(i) for i in range(1, 531)]
+            symbol = all_spacegroup_symbols[0]
+            for symbol_i in all_spacegroup_symbols:
+                if space_group_symbol == symbol_i.international_short:
+                    symbol = symbol_i
+                    break
+            
+            space_type = self._typejudge(symbol.number)
+            
+            if space_type == "Cubic":
+                return self._factorize_to_three_factors(total, "xyz")
+            else:
+                return self._factorize_to_three_factors(total)
+        except:
+            return self._factorize_to_three_factors(total)
+    
+    def _typejudge(self, number: int) -> str:
+        """判断晶体类型"""
+        if number in [1, 2]:
+            return "Triclinic"
+        elif 3 <= number <= 15:
+            return "Monoclinic"
+        elif 16 <= number <= 74:
+            return "Orthorhombic"
+        elif 75 <= number <= 142:
+            return "Tetragonal"
+        elif 143 <= number <= 167:
+            return "Trigonal"
+        elif 168 <= number <= 194:
+            return "Hexagonal"
+        elif 195 <= number <= 230:
+            return "Cubic"
+        else:
+            return "Unknown"
+    
+    def _factorize_to_three_factors(self, n: int, type_sym: str = None) -> List[Dict]:
+        """分解为三个因子"""
+        factors = []
+        
+        if type_sym == "xyz":
+            for x in range(1, n + 1):
+                if n % x == 0:
+                    remaining_n = n // x
+                    for y in range(1, remaining_n + 1):
+                        if remaining_n % y == 0 and y <= x:
+                            z = remaining_n // y
+                            if z <= y:
+                                factors.append({'x': x, 'y': y, 'z': z})
+        else:
+            for x in range(1, n + 1):
+                if n % x == 0:
+                    remaining_n = n // x
+                    for y in range(1, remaining_n + 1):
+                        if remaining_n % y == 0:
+                            z = remaining_n // y
+                            factors.append({'x': x, 'y': y, 'z': z})
+        
+        # 排序
+        def sort_key(item):
+            return (item['x'] + item['y'] + item['z'], item['z'], item['y'], item['x'])
+        
+        return sorted(factors, key=sort_key)
+    
+    def _generate_structure_list(
+        self,
+        base_structure: Structure,
+        occupation_list: List[Dict]
+    ) -> List[Structure]:
+        """生成结构列表"""
+        if not occupation_list:
+            return [base_structure.copy()]
+        
+        lcm = occupation_list[0]["denominator"]
+        structure_list = [base_structure.copy() for _ in range(lcm)]
+        
+        for entry in occupation_list:
+            numerator = entry["numerator"]
+            denominator = entry["denominator"]
+            atom_indices = entry["atom_serial"]
+            
+            for atom_idx in atom_indices:
+                occupancy_dict = self._mark_atoms_randomly(numerator, denominator)
+                original_site = base_structure.sites[atom_idx - 1]
+                element = self._get_first_non_explicit_element(original_site.species_string)
+                
+                for copy_idx, occupy in occupancy_dict.items():
+                    structure_list[copy_idx].remove_sites([atom_idx - 1])
+                    oxi_state = self._extract_oxi_state(original_site.species_string, element)
+                    
+                    if len(entry["split"]) == 1:
+                        if occupy:
+                            new_site = PeriodicSite(
+                                species=Species(element, oxi_state),
+                                coords=original_site.frac_coords,
+                                lattice=structure_list[copy_idx].lattice,
+                                to_unit_cell=True,
+                                label=original_site.label
+                            )
+                        else:
+                            species_dict = {Species(self.target_cation, 1.0): 0.0}
+                            new_site = PeriodicSite(
+                                species=species_dict,
+                                coords=original_site.frac_coords,
+                                lattice=structure_list[copy_idx].lattice,
+                                to_unit_cell=True,
+                                label=original_site.label
+                            )
+                    else:
+                        if occupy:
+                            new_site = PeriodicSite(
+                                species=Species(element, oxi_state),
+                                coords=original_site.frac_coords,
+                                lattice=structure_list[copy_idx].lattice,
+                                to_unit_cell=True,
+                                label=original_site.label
+                            )
+                        else:
+                            new_site = PeriodicSite(
+                                species=Species(entry['split'][1], oxi_state),
+                                coords=original_site.frac_coords,
+                                lattice=structure_list[copy_idx].lattice,
+                                to_unit_cell=True,
+                                label=original_site.label
+                            )
+                    
+                    structure_list[copy_idx].sites.insert(atom_idx - 1, new_site)
+        
+        return structure_list
+    
+    def _mark_atoms_randomly(self, numerator: int, denominator: int) -> Dict[int, int]:
+        """随机标记原子"""
+        if numerator > denominator:
+            raise ValueError(f"numerator ({numerator}) 不能超过 denominator ({denominator})")
+        
+        atom_dice = list(range(denominator))
+        selected_atoms = random.sample(atom_dice, numerator)
+        
+        return {atom: 1 if atom in selected_atoms else 0 for atom in atom_dice}
+    
+    def _get_first_non_explicit_element(self, species_str: str) -> str:
+        """获取第一个非目标元素"""
+        if not species_str.strip():
+            return ""
+        
+        species_parts = [part.strip() for part in species_str.split(",") if part.strip()]
+        
+        for part in species_parts:
+            element_with_charge = part.split(":")[0].strip()
+            pure_element = ''.join([c for c in element_with_charge if c.isalpha()])
+            
+            if pure_element not in self.explict_element:
+                return pure_element
+        
+        return ""
+    
+    def _extract_oxi_state(self, species_str: str, element: str) -> int:
+        """提取氧化态"""
+        species_parts = [part.strip() for part in species_str.split(",") if part.strip()]
+        
+        for part in species_parts:
+            element_with_charge = part.split(":")[0].strip()
+            
+            if element in element_with_charge:
+                charge_part = element_with_charge[len(element):]
+                
+                if not any(c.isdigit() for c in charge_part):
+                    if "+" in charge_part:
+                        return 1
+                    elif "-" in charge_part:
+                        return -1
+                    else:
+                        return 0
+                
+                sign = 1
+                if "-" in charge_part:
+                    sign = -1
+                
+                digits = ""
+                for c in charge_part:
+                    if c.isdigit():
+                        digits += c
+                
+                if digits:
+                    return sign * int(digits)
+        
+        return 0
+    
+    def _merge_structures(self, structure_list: List[Structure], merge_dict: Dict) -> Structure:
+        """合并结构"""
+        if not structure_list:
+            raise ValueError("结构列表不能为空")
+        
+        ref_lattice = structure_list[0].lattice
+        
+        total_merge = merge_dict.get("x", 1) * merge_dict.get("y", 1) * merge_dict.get("z", 1)
+        if len(structure_list) != total_merge:
+            raise ValueError(f"结构数量({len(structure_list)})与合并次数({total_merge})不匹配")
+        
+        a, b, c = ref_lattice.abc
+        alpha, beta, gamma = ref_lattice.angles
+        
+        new_a = a * merge_dict.get("x", 1)
+        new_b = b * merge_dict.get("y", 1)
+        new_c = c * merge_dict.get("z", 1)
+        new_lattice = Lattice.from_parameters(new_a, new_b, new_c, alpha, beta, gamma)
+        
+        all_sites = []
+        for i, structure in enumerate(structure_list):
+            x_offset = (i // (merge_dict.get("y", 1) * merge_dict.get("z", 1))) % merge_dict.get("x", 1)
+            y_offset = (i // merge_dict.get("z", 1)) % merge_dict.get("y", 1)
+            z_offset = i % merge_dict.get("z", 1)
+            
+            for site in structure:
+                coords = site.frac_coords.copy()
+                coords[0] = (coords[0] + x_offset) / merge_dict.get("x", 1)
+                coords[1] = (coords[1] + y_offset) / merge_dict.get("y", 1)
+                coords[2] = (coords[2] + z_offset) / merge_dict.get("z", 1)
+                all_sites.append({"species": site.species, "coords": coords})
+        
+        return Structure(
+            new_lattice,
+            [site["species"] for site in all_sites],
+            [site["coords"] for site in all_sites]
+        )
+
+
+def process_batch(
+    input_files: List[str],
+    output_dir: str,
+    needs_expansion_flags: List[bool] = None,
+    valence_yaml_path: str = None,
+    calculate_type: str = 'low',
+    target_cation: str = "Li",
+    show_progress: bool = True
+) -> List[ProcessingResult]:
+    """
+    批量处理CIF文件
+    
+    Args:
+        input_files: 输入文件列表
+        output_dir: 输出目录
+        needs_expansion_flags: 是否需要扩胞的标记列表
+        valence_yaml_path: 化合价配置文件路径
+        calculate_type: 扩胞计算精度
+        target_cation: 目标阳离子
+        show_progress: 是否显示进度
+        
+    Returns:
+        处理结果列表
+    """
+    os.makedirs(output_dir, exist_ok=True)
+    
+    processor = StructureProcessor(
+        valence_yaml_path=valence_yaml_path,
+        calculate_type=calculate_type,
+        target_cation=target_cation
+    )
+    
+    if needs_expansion_flags is None:
+        needs_expansion_flags = [False] * len(input_files)
+    
+    results = []
+    total = len(input_files)
+    
+    for i, (input_file, needs_exp) in enumerate(zip(input_files, needs_expansion_flags)):
+        if show_progress:
+            print(f"\r处理进度: {i+1}/{total} - {os.path.basename(input_file)}", end="")
+        
+        result = processor.process_file(input_file, output_dir, needs_exp)
+        results.append(result)
+    
+    if show_progress:
+        print()
+    
+    return results

tool/Li/Br+O.yaml

@@ -0,0 +1,5 @@
+SPECIE: Li+
+ANION: Br
+PERCO_R: 0.45
+NEIGHBOR: 1.8
+LONG: 2.2

tool/Li/Cl+O.yaml

@@ -0,0 +1,5 @@
+SPECIE: Li+
+ANION: Cl
+PERCO_R: 0.45
+NEIGHBOR: 1.8
+LONG: 2.2