nep框架重构 00.md增加自定义POSCAR，03.train增加了并入之前的训练结果

src/workflow.py

@@ -48,13 +48,41 @@ class Workflow:
                 # ==========================
                 if step_name == "00.md":
                     step_dir = os.path.join(iter_path, "00.md")
-
-                    # 1. 初始化 model.xyz (仅做一次)
                     task_id_init = f"{iter_name}.00.md.init"
 
-                    if iter_id == 0:
+                    if not os.path.exists(step_dir):
-                        if not self.tracker.is_done(task_id_init):
                         os.makedirs(step_dir, exist_ok=True)
+
+                    # 获取本轮是否定义了自定义 POSCAR
+                    custom_poscar = iteration.get('custom_poscar')
+
+                    if not self.tracker.is_done(task_id_init):
+                        # === 情况 A: 用户指定了自定义 POSCAR (优先级最高) ===
+                        if custom_poscar:
+                            self.logger.info(f"Using Custom POSCAR for this iteration: {custom_poscar}")
+                            poscar_src = os.path.join(self.data_dir, custom_poscar)
+
+                            if os.path.exists(poscar_src):
+                                # 复制并重命名为 config 中定义的标准名 (为了方便 gpumdkit 处理)
+                                std_poscar_name = self.param['files']['poscar']
+                                shutil.copy(poscar_src, os.path.join(step_dir, std_poscar_name))
+
+                                # 调用 gpumdkit 转化
+                                atom_labels = self.param['files'].get('label', '')
+                                kit_path = self.machine.config['paths'].get('gpumdkit', 'gpumdkit.sh')
+                                cmd = f"{kit_path} -addlabel {std_poscar_name} {atom_labels}"
+
+                                if run_cmd_with_log(cmd, step_dir, "init.log"):
+                                    self.tracker.mark_done(task_id_init)
+                                else:
+                                    self.logger.error("Custom POSCAR initialization failed.")
+                                    return
+                            else:
+                                self.logger.error(f"Custom POSCAR not found in data dir: {poscar_src}")
+                                return
+
+                        # === 情况 B: 第一轮且无自定义 (使用默认 POSCAR) ===
+                        elif iter_id == 0:
                             poscar_name = self.param['files']['poscar']
                             poscar_src = os.path.join(self.data_dir, poscar_name)
 
@@ -62,28 +90,20 @@ class Workflow:
                                 shutil.copy(poscar_src, os.path.join(step_dir, poscar_name))
                                 atom_labels = self.param['files'].get('label', '')
                                 kit_path = self.machine.config['paths'].get('gpumdkit', 'gpumdkit.sh')
-
                                 cmd = f"{kit_path} -addlabel {poscar_name} {atom_labels}"
-                                self.logger.info(f"Initializing model.xyz...")
 
                                 if run_cmd_with_log(cmd, step_dir, "init.log"):
                                     self.tracker.mark_done(task_id_init)
                                 else:
-                                    self.logger.error("Initialization failed. Check iter_00/00.md/init.log")
+                                    self.logger.error("Initialization failed.")
                                     return
                             else:
-                                self.logger.error("POSCAR missing.")
+                                self.logger.error("Default POSCAR missing.")
                                 return
-                        else:
-                            self.logger.info("Skipping Init (Already Done).")
-                    else:
-                        # --- [新增逻辑] 后续轮次：从上一轮复制 model.xyz ---
 
-                        # 1. 【核心修复】必须先确保目标文件夹存在！
+                        # === 情况 C: 后续轮次且无自定义 (继承上一轮) ===
-                        if not os.path.exists(step_dir):
+                        else:
-                            os.makedirs(step_dir, exist_ok=True)
+                            # 只有当 model.xyz 不存在时才去复制
-
-                        # 2. 检查当前目录是否已有 model.xyz
                             if not os.path.exists(os.path.join(step_dir, "model.xyz")):
                                 prev_iter_name = f"iter_{iter_id - 1:02d}"
                                 prev_model_src = os.path.join(self.workspace, prev_iter_name, "00.md", "model.xyz")
@@ -91,9 +111,12 @@ class Workflow:
                                 if os.path.exists(prev_model_src):
                                     self.logger.info(f"Copying model.xyz from {prev_iter_name}...")
                                     shutil.copy(prev_model_src, os.path.join(step_dir, "model.xyz"))
+                                    self.tracker.mark_done(task_id_init)  # 标记完成
                                 else:
                                     self.logger.error(f"Previous model.xyz not found: {prev_model_src}")
                                     return
+                            else:
+                                self.tracker.mark_done(task_id_init)
                     # 确保 gpumdkit 路径可用
                     kit_path = self.machine.config['paths'].get('gpumdkit', 'gpumdkit.sh')
 
@@ -548,11 +571,23 @@ class Workflow:
                                 with open(self.current_train_set, 'r') as infile:
                                     shutil.copyfileobj(infile, outfile)
 
-                            # B. 写入新数据 (来自本轮 SCF)
+                            # B. [新增] 注入 data 中的额外数据 (仅在第一轮注入，防止重复)
-                            # 尝试获取变量，如果变量丢失则尝试从路径推断
+                            # 如果 extra_train_data 存在，且当前是第一轮 (或者 current_train_set 还没建立)
+                            extra_files = self.param['files'].get('extra_train_data', [])
+                            if iter_id == 0 and extra_files:
+                                self.logger.info(f"Injecting extra training data: {extra_files}")
+                                for xyz_file in extra_files:
+                                    src_xyz = os.path.join(self.data_dir, xyz_file)
+                                    if os.path.exists(src_xyz):
+                                        self.logger.info(f"  -> Appending {xyz_file}")
+                                        with open(src_xyz, 'r') as infile:
+                                            shutil.copyfileobj(infile, outfile)
+                                    else:
+                                        self.logger.warning(f"Extra data file not found: {src_xyz}")
+
+                            # C. 写入新数据 (来自本轮 SCF)
                             new_data = getattr(self, 'new_data_chunk', None)
                             if not new_data:
-                                # 推断路径: iter_XX/02.scf/NEPdataset-multiple_frames/NEP-dataset.xyz
                                 new_data = os.path.join(iter_path, "02.scf", "NEPdataset-multiple_frames",
                                                         "NEP-dataset.xyz")
 
@@ -561,12 +596,14 @@ class Workflow:
                                 with open(new_data, 'r') as infile:
                                     shutil.copyfileobj(infile, outfile)
                             else:
-                                if iter_id == 0 and not os.path.exists(self.current_train_set):
+                                # 只有在既没有旧数据，又没有额外数据，也没有新数据时才报错
-                                    self.logger.error("No training data available (neither previous nor new).")
+                                has_data = (os.path.exists(self.current_train_set) or
+                                            (iter_id == 0 and extra_files))
+                                if not has_data:
+                                    self.logger.error("No training data available at all.")
                                     return
-                                else:
+                                elif not new_data or not os.path.exists(new_data):
-                                    self.logger.warning("No new data found from SCF step. Training on old data only.")
+                                    self.logger.warning("No new SCF data found. Training on existing/extra data only.")
-
                         # 更新全局变量指向最新的 train.xyz，供下一轮使用
                         self.current_train_set = train_xyz_path