356 lines
14 KiB
Python
356 lines
14 KiB
Python
from typing import List, Dict
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from pymatgen.core.structure import Structure
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from pymatgen.analysis.local_env import VoronoiNN
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import numpy as np
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def check_real(nearest):
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real_nearest = []
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for site in nearest:
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if np.all((site.frac_coords >= 0) & (site.frac_coords <= 1)):
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real_nearest.append(site)
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return real_nearest
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def special_check_for_3(site, nearest):
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real_nearest = []
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distances = []
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for site2 in nearest:
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distance = np.linalg.norm(np.array(site.frac_coords) - np.array(site2.frac_coords))
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distances.append(distance)
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sorted_indices = np.argsort(distances)
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for index in sorted_indices[:3]:
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real_nearest.append(nearest[index])
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return real_nearest
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def CS_catulate(
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struct,
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sp: str = 'Li',
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anion: List[str] = ['O'],
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tol: float = 0,
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cutoff: float = 3.0,
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notice: bool = False
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) -> Dict[str, Dict[str, int]]:
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"""
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计算结构中不同类型阳离子多面体之间的共享关系（角、边、面共享）。
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该函数会分别计算以下三种情况的共享数量：
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1. 目标原子 vs 目标原子 (e.g., Li-Li)
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2. 目标原子 vs 其他阳离子 (e.g., Li-X)
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3. 其他阳离子 vs 其他阳离子 (e.g., X-Y)
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参数:
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struct (Structure): 输入的pymatgen结构对象。
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sp (str): 目标元素符号，默认为 'Li'。
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anion (list): 阴离子元素符号列表，默认为 ['O']。
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tol (float): VoronoiNN 的容差。对于Li，通常设为0。
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cutoff (float): VoronoiNN 的截断距离。对于Li，通常设为3.0。
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notice (bool): 是否打印详细的共享信息。
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返回:
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dict: 一个字典，包含三类共享关系的统计结果。
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键 "sp_vs_sp", "sp_vs_other", "other_vs_other" 分别对应上述三种情况。
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每个键的值是另一个字典，统计了共享2个(边)、3个(面)等情况的数量。
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例如: {'sp_vs_sp': {'1': 10, '2': 4}, 'sp_vs_other': ...}
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共享1个阴离子为角共享，2个为边共享，3个为面共享。
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"""
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# 初始化 VoronoiNN 对象
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voro_nn = VoronoiNN(tol=tol, cutoff=cutoff)
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# 1. 分类存储所有阳离子的近邻阴离子信息
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target_sites_info = []
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other_cation_sites_info = []
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for index, site in enumerate(struct.sites):
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# 跳过阴离子本身
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if site.species.chemical_system in anion:
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continue
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# 获取当前位点的近邻阴离子
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try:
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# 使用 get_nn_info 更直接
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nn_info = voro_nn.get_nn_info(struct, index)
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nearest_anions = [
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nn["site"] for nn in nn_info
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if nn["site"].species.chemical_system in anion
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]
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except Exception as e:
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print(f"Warning: Could not get neighbors for site {index} ({site.species_string}): {e}")
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continue
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if not nearest_anions:
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continue
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# 整理信息
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site_info = {
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'index': index,
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'element': site.species.chemical_system,
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'nearest_anion_indices': {nn.index for nn in nearest_anions}
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}
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# 根据是否为目标原子进行分类
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if site.species.chemical_system == sp:
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target_sites_info.append(site_info)
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else:
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other_cation_sites_info.append(site_info)
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# 2. 初始化结果字典
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# 共享数量key: 1-角, 2-边, 3-面
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results = {
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"sp_vs_sp": {"1": 0, "2": 0, "3": 0, "4": 0},
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"sp_vs_other": {"1": 0, "2": 0, "3": 0, "4": 0},
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"other_vs_other": {"1": 0, "2": 0, "3": 0, "4": 0},
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}
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# 3. 计算不同类别之间的共享关系
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# 3.1 目标原子 vs 目标原子 (sp_vs_sp)
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for i in range(len(target_sites_info)):
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for j in range(i + 1, len(target_sites_info)):
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atom_i = target_sites_info[i]
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atom_j = target_sites_info[j]
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shared_anions = atom_i['nearest_anion_indices'].intersection(atom_j['nearest_anion_indices'])
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shared_count = len(shared_anions)
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if shared_count > 0 and str(shared_count) in results["sp_vs_sp"]:
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results["sp_vs_sp"][str(shared_count)] += 1
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if notice:
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print(
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f"[Li-Li] Atom {atom_i['index']} and {atom_j['index']} share {shared_count} anions: {shared_anions}")
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# 3.2 目标原子 vs 其他阳离子 (sp_vs_other)
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for atom_sp in target_sites_info:
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for atom_other in other_cation_sites_info:
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shared_anions = atom_sp['nearest_anion_indices'].intersection(atom_other['nearest_anion_indices'])
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shared_count = len(shared_anions)
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if shared_count > 0 and str(shared_count) in results["sp_vs_other"]:
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results["sp_vs_other"][str(shared_count)] += 1
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if notice:
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print(
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f"[Li-Other] Atom {atom_sp['index']} and {atom_other['index']} share {shared_count} anions: {shared_anions}")
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# 3.3 其他阳离子 vs 其他阳离子 (other_vs_other)
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for i in range(len(other_cation_sites_info)):
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for j in range(i + 1, len(other_cation_sites_info)):
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atom_i = other_cation_sites_info[i]
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atom_j = other_cation_sites_info[j]
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shared_anions = atom_i['nearest_anion_indices'].intersection(atom_j['nearest_anion_indices'])
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shared_count = len(shared_anions)
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if shared_count > 0 and str(shared_count) in results["other_vs_other"]:
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results["other_vs_other"][str(shared_count)] += 1
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if notice:
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print(
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f"[Other-Other] Atom {atom_i['index']} and {atom_j['index']} share {shared_count} anions: {shared_anions}")
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return results
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def CS_catulate_old(struct, sp='Li', anion=['O'], tol=0, cutoff=3.0,notice=False,ID=None):
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"""
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计算结构中目标元素与最近阴离子的共享关系。
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参数:
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struct (Structure): 输入结构。
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sp (str): 目标元素符号，默认为 'Li'。
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anion (list): 阴离子列表，默认为 ['O']。
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tol (float): VoronoiNN 的容差，默认为 0。
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cutoff (float): VoronoiNN 的截断距离，默认为 3.0。
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返回:
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list: 包含每个目标位点及其最近阴离子索引的列表。
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"""
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# 初始化 VoronoiNN 对象
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if sp=='Li':
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tol = 0
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cutoff = 3.0
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voro_nn = VoronoiNN(tol=tol, cutoff=cutoff)
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# 初始化字典，用于统计共享关系
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shared_count = {"2": 0, "3": 0,"4":0,"5":0,"6":0}
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# 存储结果的列表
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atom_dice = []
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# 遍历结构中的每个位点
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for index,site in enumerate(struct.sites):
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# 跳过阴离子位点
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if site.species.chemical_system in anion:
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continue
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# 跳过Li原子
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if site.species.chemical_system == sp:
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continue
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# 获取 Voronoi 多面体信息
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voro_info = voro_nn.get_voronoi_polyhedra(struct, index)
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# 找到最近的阴离子位点
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nearest_anions = [
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nn_info["site"] for nn_info in voro_info.values()
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if nn_info["site"].species.chemical_system in anion
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]
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# 如果没有找到最近的阴离子，跳过
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if not nearest_anions:
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print(f"No nearest anions found for {ID} site {index}.")
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continue
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if site.species.chemical_system == 'B' or site.species.chemical_system == 'N':
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nearest_anions = special_check_for_3(site,nearest_anions)
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nearest_anions = check_real(nearest_anions)
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# 将结果添加到 atom_dice 列表中
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atom_dice.append({
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'index': index,
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'nearest_index': [nn.index for nn in nearest_anions]
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})
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# 枚举 atom_dice 中的所有原子对
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for i, atom_i in enumerate(atom_dice):
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for j, atom_j in enumerate(atom_dice[i + 1:], start=i + 1):
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# 获取两个原子的最近阴离子索引
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nearest_i = set(atom_i['nearest_index'])
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nearest_j = set(atom_j['nearest_index'])
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# 比较最近阴离子的交集大小
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shared_count_key = str(len(nearest_i & nearest_j))
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# 更新字典中的计数
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if shared_count_key in shared_count:
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shared_count[shared_count_key] += 1
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if notice:
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if shared_count_key=='2':
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print(f"{atom_j['index']}与{atom_i['index']}之间存在共线")
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print(f"共线的阴离子为{nearest_i & nearest_j}")
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if shared_count_key=='3':
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print(f"{atom_j['index']}与{atom_i['index']}之间存在共面")
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print(f"共面的阴离子为{nearest_i & nearest_j}")
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# # 最后将字典中的值除以 2，因为每个共享关系被计算了两次
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# for key in shared_count.keys():
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# shared_count[key] //= 2
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return shared_count
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def CS_count(struct, sharing_results: Dict[str, Dict[str, int]], sp: str = 'Li') -> float:
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"""
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分析多面体共享结果，计算平均每个目标原子参与的共享阴离子数。
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这个函数是 calculate_polyhedra_sharing 的配套函数。
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参数:
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struct (Structure): 输入的pymatgen结构对象，用于统计目标原子总数。
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sharing_results (dict): 来自 calculate_polyhedra_sharing 函数的输出结果。
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sp (str): 目标元素符号，默认为 'Li'。
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返回:
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float: 平均每个目标原子sp参与的共享阴离子数量。
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例如，结果为2.5意味着平均每个Li原子通过共享与其他阳离子
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（包括Li和其他阳离子）连接了2.5个阴离子。
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"""
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# 1. 统计结构中目标原子的总数
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target_atom_count = 0
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for site in struct.sites:
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if site.species.chemical_system == sp:
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target_atom_count += 1
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# 如果结构中没有目标原子，直接返回0，避免除以零错误
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if target_atom_count == 0:
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return 0.0
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# 2. 计算加权的共享阴离子总数
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total_shared_anions = 0
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# 处理 sp_vs_sp (例如 Li-Li) 的共享
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# 每个共享关系涉及两个目标原子，所以权重需要乘以 2
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if "sp_vs_sp" in sharing_results:
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sp_vs_sp_counts = sharing_results["sp_vs_sp"]
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for num_shared_str, count in sp_vs_sp_counts.items():
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num_shared = int(num_shared_str)
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# 权重 = 共享阴离子数 * 涉及的目标原子数 (2) * 出现次数
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total_shared_anions += num_shared * 2 * count
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# 处理 sp_vs_other (例如 Li-X) 的共享
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# 每个共享关系涉及一个目标原子，所以权重乘以 1
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if "sp_vs_other" in sharing_results:
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sp_vs_other_counts = sharing_results["sp_vs_other"]
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for num_shared_str, count in sp_vs_other_counts.items():
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num_shared = int(num_shared_str)
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# 权重 = 共享阴离子数 * 涉及的目标原子数 (1) * 出现次数
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total_shared_anions += num_shared * 1 * count
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# 3. 计算平均值
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# 平均每个目标原子参与的共享阴离子数 = 总的加权共享数 / 目标原子总数
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average_sharing_per_atom = total_shared_anions / target_atom_count
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return average_sharing_per_atom
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def CS_count_old(struct, shared_count, sp='Li'):
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count = 0
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for site in struct.sites:
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if site.species.chemical_system == sp:
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count += 1 # 累加符合条件的原子数量
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CS_count = 0
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for i in range(2, 7): # 遍历范围 [2, 3, 4, 5]
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if str(i) in shared_count: # 检查键是否存在
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CS_count += shared_count[str(i)] * i # 累加计算结果
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if count > 0: # 防止除以零
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CS_count /= count # 平均化结果
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else:
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CS_count = 0 # 如果 count 为 0，直接返回 0
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return CS_count
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def check_only_corner_sharing(sharing_results: Dict[str, Dict[str, int]]) -> int:
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"""
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检查目标原子(sp)是否只参与了角共享（共享1个阴离子）。
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该函数是 calculate_polyhedra_sharing 的配套函数。
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参数:
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sharing_results (dict): 来自 calculate_polyhedra_sharing 函数的输出结果。
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返回:
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int:
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- 1: 如果 sp 的共享关系中，边共享(2)、面共享(3)等数量均为0，
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并且至少存在一个角共享(1)。
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- 0: 如果 sp 存在任何边、面等共享，或者没有任何共享关系。
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"""
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# 提取与目标原子 sp 相关的共享数据
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sp_vs_sp_counts = sharing_results.get("sp_vs_sp", {})
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sp_vs_other_counts = sharing_results.get("sp_vs_other", {})
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# 1. 检查是否存在任何边共享、面共享等 (共享数 > 1)
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# 检查 sp-sp 的共享
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for num_shared_str, count in sp_vs_sp_counts.items():
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if int(num_shared_str) > 1 and count > 0:
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return 0 # 发现了边/面共享，立即返回 0
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# 检查 sp-other 的共享
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for num_shared_str, count in sp_vs_other_counts.items():
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if int(num_shared_str) > 1 and count > 0:
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return 0 # 发现了边/面共享，立即返回 0
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# 2. 检查是否存在至少一个角共享 (共享数 == 1)
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# 运行到这里，说明已经没有任何边/面共享了。
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# 现在需要确认是否真的存在角共享，而不是完全没有共享。
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corner_share_sp_sp = sp_vs_sp_counts.get("1", 0) > 0
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corner_share_sp_other = sp_vs_other_counts.get("1", 0) > 0
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if corner_share_sp_sp or corner_share_sp_other:
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return 1 # 确认只存在角共享
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else:
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return 0 # 没有任何共享关系，也返回 0
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# structure = Structure.from_file("../data/0921/wjy_001.cif")
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# a = CS_catulate(structure,notice=True)
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# b = CS_count(structure,a)
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# print(f"{a}\n{b}")
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# print(check_only_corner_sharing(a)) |