CSM计算
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134
corner-sharing/utils/CS_analyse.py
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134
corner-sharing/utils/CS_analyse.py
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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(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.specie.symbol in anion:
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continue
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# 跳过Li原子
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if site.specie.symbol == 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"].specie.symbol 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.specie.symbol == 'B' or site.specie.symbol == '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, 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.specie.symbol == 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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structure = Structure.from_file("data/CS_Table1/Li3Al(BO3)2_mp-6097_computed.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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@@ -47,28 +47,19 @@ class HiddenPrints:
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def non_elements(struct, sp='Li'):
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'''
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struct : structure object from Pymatgen
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"""
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struct : 必须是一个有序结构
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sp : the mobile specie
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returns the structure with all of the mobile specie (Li) removed
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'''
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num_li = struct.species.count(Element(sp))
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species = list(set(struct.species))
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try:
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species.remove(Element("O"))
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except ValueError:
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print("没有O")
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try:
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species.remove(Element("S"))
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except ValueError:
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print("没有S")
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try:
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species.remove(Element("N"))
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except ValueError:
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print("没有N")
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returns a new structure containing only the framework anions (O, S, N).
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"""
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anions_to_keep = {"O", "S", "N"}
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stripped = struct.copy()
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stripped.remove_species(species)
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stripped = stripped.get_sorted_structure(reverse=True)
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species_to_remove = [el.symbol for el in stripped.composition.elements
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if el.symbol not in anions_to_keep]
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if species_to_remove:
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stripped.remove_species(species_to_remove)
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return stripped
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@@ -160,22 +151,43 @@ def site_env(coord, struct, sp="Li", envtype='both'):
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def extract_sites(struct, sp="Li", envtype='both'):
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'''
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"""
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struct : structure object from Pymatgen
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envtype : 'tet', 'oct', or 'both'
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sp : target element to analyze environment
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'''
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"""
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envlist = []
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for i in range(len(struct.sites)):
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if struct.sites[i].specie != Element(sp):
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continue
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site = struct.sites[i]
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singleenv = site_env(site.frac_coords, struct, sp, envtype)
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envlist.append({'frac_coords': site.frac_coords, 'type': singleenv['type'], 'csm': singleenv['csm'],
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'volume': singleenv['vol']})
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return envlist
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# --- 关键修改：直接遍历原始结构，即使它是无序的 ---
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# 我们不再调用 get_sorted_structure()
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# 我们只关心那些含有目标元素 sp 的位点
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# 遍历每一个位点 (site)
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for i, site in enumerate(struct):
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# 检查当前位点的组分(site.species)中是否包含我们感兴趣的元素(sp)
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# site.species.elements 返回该位点上的元素列表，例如 [Element Li, Element Fe]
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# [el.symbol for el in site.species.elements] 将其转换为符号列表 ['Li', 'Fe']
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site_elements = [el.symbol for el in site.species.elements]
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if sp in site_elements:
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# 如果找到了Li，我们就对这个位点进行环境分析
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# 注意：我们将原始的、可能无序的 struct 传递给 site_env
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# 因为 site_env 内部的函数 (如 LocalGeometryFinder) 知道如何处理它
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# 为了让下游函数（特别是 non_elements）能够工作，
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# 我们在这里创建一个一次性的、临时的有序结构副本给它
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# 这可以避免我们之前遇到的所有 'ordered structures only' 错误
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temp_ordered_struct = struct.get_sorted_structure()
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singleenv = site_env(site.frac_coords, temp_ordered_struct, sp, envtype)
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envlist.append({'frac_coords': site.frac_coords, 'type': singleenv['type'], 'csm': singleenv['csm'],
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'volume': singleenv['vol']})
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if not envlist:
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print(f"警告: 在结构中未找到元素 {sp} 的占位。")
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return envlist
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def export_envs(envlist, sp='Li', envtype='both', fname=None):
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'''
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@@ -193,6 +205,6 @@ def export_envs(envlist, sp='Li', envtype='both', fname=None):
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f.write("Site index " + str(index) + ": " + str(i) + '\n')
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struct = Structure.from_file("../data/31960.cif")
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site_info = extract_sites(struct, envtype="both")
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export_envs(site_info, sp="Li", envtype="both")
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# struct = Structure.from_file("../data/0921/wjy_475.cif")
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# site_info = extract_sites(struct, envtype="both")
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# export_envs(site_info, sp="Li", envtype="both")
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