import pymatgen.core as mg from pymatgen.io.cif import CifParser from pymatgen.transformations.standard_transformations import SupercellTransformation import random import os def create_ordered_structure_from_disordered(disordered_structure): """ 手动将包含部分占位的无序结构转换为有序结构，借鉴plus.py的思路。 """ s = disordered_structure.copy() # 识别需要处理的部分占位 # 根据 model3.cif, Y2(z≈0.488, occ=0.75), Y3(z≈-0.065, occ=0.25), Li2(z≈0.5, occ=0.5) [model3.cif] y2_indices, y3_indices, li2_indices = [], [], [] for i, site in enumerate(s.sites): # 使用z坐标来识别特定的部分占位 z = site.frac_coords[2] if site.species_string == "Y": if abs(z - 0.488) < 0.05: y2_indices.append(i) elif abs(z - (-0.065)) < 0.05 or abs(z - (1 - 0.065)) < 0.05: y3_indices.append(i) elif site.species_string == "Li": if abs(z - 0.5) < 0.05: li2_indices.append(i) # 根据占位率随机选择要保留的原子 def choose_keep(indices, keep_fraction): num_to_keep = int(round(len(indices) * keep_fraction)) return set(random.sample(indices, num_to_keep)) keep_y2 = choose_keep(y2_indices, 0.75) keep_y3 = choose_keep(y3_indices, 0.25) keep_li2 = choose_keep(li2_indices, 0.50) # 找出所有需要删除的原子索引 to_remove_indices = [i for i in y2_indices if i not in keep_y2] to_remove_indices.extend([i for i in y3_indices if i not in keep_y3]) to_remove_indices.extend([i for i in li2_indices if i not in keep_li2]) # 从后往前删除，避免索引错位 s.remove_sites(sorted(to_remove_indices, reverse=True)) # --- 关键修复步骤 --- # 最终清理，确保所有位点都是有序的 for i, site in enumerate(s.sites): if not site.is_ordered: # 将Composition对象转换为字典，然后找到占位率最高的元素 [plus.py] species_dict = site.species.as_dict() main_specie = max(species_dict.items(), key=lambda item: item[1])[0] s.replace(i, main_specie) return s def create_supercells_from_file(cif_path, output_path="."): """ 根据给定的CIF文件路径，生成三种不同尺寸和缺陷的超胞，并保存为POSCAR文件。 """ if not os.path.exists(cif_path): print(f"错误: 文件 '{cif_path}' 不存在。") return print(f"正在从 {cif_path} 读取结构...") parser = CifParser(cif_path) disordered_structure = parser.parse_structures(primitive=False)[0] structure = create_ordered_structure_from_disordered(disordered_structure) print(f"成功将无序结构转换为一个包含 {len(structure)} 个原子的有序单胞。") os.makedirs(output_path, exist_ok=True) # 任务一：生成60原子超胞 (无缺陷) print("\n--- 正在生成 60原子无缺陷超胞 (1x1x2) ---") tf_60 = SupercellTransformation([[1, 0, 0], [0, 1, 0], [0, 0, 2]]) sc_60_no_defect = tf_60.apply_transformation(structure) print(f"原子总数: {len(sc_60_no_defect)}, 化学式: {sc_60_no_defect.composition.reduced_formula}") sc_60_no_defect.to(fmt="poscar", filename=os.path.join(output_path, "POSCAR_60_no_defect")) print(f"已保存文件: {os.path.join(output_path, 'POSCAR_60_no_defect')}") # 任务二：生成60原子超胞 (含一对反位缺陷) print("\n--- 正在生成 60原子含一对反位缺陷超胞 ---") sc_60_defect = sc_60_no_defect.copy() li_indices = [i for i, site in enumerate(sc_60_defect.sites) if site.species_string == 'Li'] y_indices = [i for i, site in enumerate(sc_60_defect.sites) if site.species_string == 'Y'] if li_indices and y_indices: li_swap_idx, y_swap_idx = random.choice(li_indices), random.choice(y_indices) sc_60_defect.replace(li_swap_idx, "Y") sc_60_defect.replace(y_swap_idx, "Li") print(f"成功引入一对反位缺陷。浓度: {2 / (len(li_indices) + len(y_indices)) * 100:.2f}%") sc_60_defect.to(fmt="poscar", filename=os.path.join(output_path, "POSCAR_60_antisite_defect")) print(f"已保存文件: {os.path.join(output_path, 'POSCAR_60_antisite_defect')}") # 任务三：生成90原子超胞 (含一对反位缺陷) print("\n--- 正在生成 90原子含一对反位缺陷超胞 ---") tf_90 = SupercellTransformation([[1, 0, 0], [0, 1, 0], [0, 0, 3]]) sc_90_no_defect = tf_90.apply_transformation(structure) sc_90_defect = sc_90_no_defect.copy() li_indices = [i for i, site in enumerate(sc_90_defect.sites) if site.species_string == 'Li'] y_indices = [i for i, site in enumerate(sc_90_defect.sites) if site.species_string == 'Y'] if li_indices and y_indices: li_swap_idx, y_swap_idx = random.choice(li_indices), random.choice(y_indices) sc_90_defect.replace(li_swap_idx, "Y") sc_90_defect.replace(y_swap_idx, "Li") print(f"原子总数: {len(sc_90_defect)}, 浓度: {2 / (len(li_indices) + len(y_indices)) * 100:.2f}%") sc_90_defect.to(fmt="poscar", filename=os.path.join(output_path, "POSCAR_90_antisite_defect")) print(f"已保存文件: {os.path.join(output_path, 'POSCAR_90_antisite_defect')}") def create_ordered_p3ma_structure(disordered_structure): """ 手动将P3ma相的无序结构（包含Y2, Y3, Li2的部分占位）转换为有序结构。 """ s = disordered_structure.copy() # 根据 model3.cif, 识别Y2(z≈0.488, occ=0.75), Y3(z≈-0.065, occ=0.25), Li2(z≈0.5, occ=0.5) [model3.cif] y2_indices, y3_indices, li2_indices = [], [], [] for i, site in enumerate(s.sites): z = site.frac_coords[2] if site.species_string == "Y": if abs(z - 0.488) < 0.05: y2_indices.append(i) elif abs(z - (-0.065)) < 0.05 or abs(z - (1 - 0.065)) < 0.05: y3_indices.append(i) elif site.species_string == "Li": if abs(z - 0.5) < 0.05: li2_indices.append(i) # 根据占位率随机选择要保留的原子 def choose_keep(indices, keep_fraction): num_to_keep = int(round(len(indices) * keep_fraction)) return set(random.sample(indices, num_to_keep)) keep_y2 = choose_keep(y2_indices, 0.75) keep_y3 = choose_keep(y3_indices, 0.25) keep_li2 = choose_keep(li2_indices, 0.50) # 找出所有需要删除的原子索引 to_remove_indices = [i for i in y2_indices if i not in keep_y2] to_remove_indices.extend([i for i in y3_indices if i not in keep_y3]) to_remove_indices.extend([i for i in li2_indices if i not in keep_li2]) s.remove_sites(sorted(to_remove_indices, reverse=True)) # 最终清理，确保所有位点都是有序的 for i, site in enumerate(s.sites): if not site.is_ordered: species_dict = site.species.as_dict() main_specie = max(species_dict.items(), key=lambda item: item[1])[0] s.replace(i, main_specie) return s def create_multiple_p3ma_supercells(cif_path, num_configs=5, output_path="."): """ 读取P3ma相CIF，为不同尺寸的超胞生成多个具有不同反位缺陷位置的构型。 """ if not os.path.exists(cif_path): print(f"错误: 文件 '{cif_path}' 不存在。") return print(f"正在从 {cif_path} 读取P3ma结构...") parser = CifParser(cif_path) disordered_structure = parser.parse_structures(primitive=False)[0] structure = create_ordered_p3ma_structure(disordered_structure) print(f"成功将无序P3ma结构转换为一个包含 {len(structure)} 个原子的有序单胞。") os.makedirs(output_path, exist_ok=True) target_sizes = [60, 90] for size in target_sizes: print(f"\n--- 正在为约 {size} 原子的版本生成 {num_configs} 个不同构型 ---") # 1. 构建基准超胞 if size == 60: tf = SupercellTransformation([[1, 0, 0], [0, 1, 0], [0, 0, 2]]) filename_suffix = "60_approx" else: # size == 90 tf = SupercellTransformation([[1, 0, 0], [0, 1, 0], [0, 0, 3]]) filename_suffix = "90_approx" base_supercell = tf.apply_transformation(structure) print(f"已生成基准超胞，实际原子数: {len(base_supercell)}") li_indices = [i for i, site in enumerate(base_supercell.sites) if site.species_string == 'Li'] y_indices = [i for i, site in enumerate(base_supercell.sites) if site.species_string == 'Y'] if not li_indices or not y_indices: print("错误：在超胞中未找到足够的Li或Y原子来引入缺陷。") continue # 2. 循环生成多个独特的缺陷构型 used_pairs = set() for i in range(num_configs): defect_supercell = base_supercell.copy() # 确保随机选择的交换对是全新的 # 增加一个尝试次数上限，防止在原子数很少时陷入死循环 max_tries = len(li_indices) * len(y_indices) for _ in range(max_tries): li_swap_idx = random.choice(li_indices) y_swap_idx = random.choice(y_indices) pair = tuple(sorted((li_swap_idx, y_swap_idx))) if pair not in used_pairs: used_pairs.add(pair) break else: print(f" 警告: 未能找到更多独特的交换对，已停止在第 {i} 个构型。") break # 引入缺陷 defect_supercell.replace(li_swap_idx, "Y") defect_supercell.replace(y_swap_idx, "Li") print(f" 配置 {i}: 成功引入一对反位缺陷 (Li at index {li_swap_idx} <-> Y at index {y_swap_idx})。") # 3. 保存为带编号的POSCAR文件 poscar_filename = f"POSCAR_P3ma_{filename_suffix}_antisite_defect_{i}" poscar_path = os.path.join(output_path, poscar_filename) defect_supercell.to(fmt="poscar", filename=poscar_path) print(f" 已保存文件: {poscar_path}") if __name__ == '__main__': # --- 使用方法 --- # 1. 将您的CIF文件保存，例如命名为 "Li3YCl6.cif" # 2. 将文件名作为参数传递给函数 cif_file_path = "data/P3ma/model3.cif" # 修改为您的CIF文件名 output_directory = "raw/P3ma/output" # 可以指定一个输出目录 # create_supercells_from_file(cif_file_path, output_directory) create_multiple_p3ma_supercells(cif_file_path,output_path=output_directory) print("所有任务完成！")