115 lines
4.5 KiB
Python
115 lines
4.5 KiB
Python
import pymatgen.core as mg
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from pymatgen.io.cif import CifParser
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from pymatgen.transformations.standard_transformations import SupercellTransformation
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import random
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import os
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def create_ordered_pnma_structure(disordered_structure):
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"""
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手动将Pnma相的无序结构（主要为Li的部分占位）转换为有序结构。
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"""
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s = disordered_structure.copy()
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# 根据origin.cif, Li位点的占位率为0.75 [5]
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partial_li_indices = [i for i, site in enumerate(s.sites) if "Li" in site.species and not site.is_ordered]
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# 根据0.75的占位率随机选择要保留的Li原子
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num_to_keep = int(round(len(partial_li_indices) * 0.75))
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keep_indices = set(random.sample(partial_li_indices, num_to_keep))
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# 找出需要删除的原子索引
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to_remove_indices = [i for i in partial_li_indices if i not in keep_indices]
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s.remove_sites(sorted(to_remove_indices, reverse=True))
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# 重新创建一个新的、完全有序的结构，避免任何副作用
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ordered_species = []
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ordered_coords = []
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for site in s.sites:
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# 只取每个位点的主要元素
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main_specie = site.species.elements[0]
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ordered_species.append(main_specie)
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ordered_coords.append(site.frac_coords)
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final_structure = mg.Structure(s.lattice, ordered_species, ordered_coords)
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return final_structure
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def create_multiple_pnma_supercells(cif_path, num_configs=3, output_path="."):
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"""
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读取Pnma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。
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"""
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if not os.path.exists(cif_path):
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print(f"错误: 文件 '{cif_path}' 不存在。")
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return
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print(f"正在从 {cif_path} 读取Pnma结构...")
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parser = CifParser(cif_path)
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disordered_structure = parser.parse_structures(primitive=False)[0]
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structure = create_ordered_pnma_structure(disordered_structure)
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print(f"成功将无序Pnma结构转换为一个包含 {len(structure)} 个原子的有序单胞。")
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os.makedirs(output_path, exist_ok=True)
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target_sizes = [60, 90]
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for size in target_sizes:
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print(f"\n--- 正在为约 {size} 原子的版本生成 {num_configs} 个不同构型 ---")
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# 1. 构建基准超胞
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if size == 60:
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tf = SupercellTransformation([[1, 0, 0], [0, 1, 0], [0, 0, 2]])
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filename_suffix = "60_approx"
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else: # size == 90
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tf = SupercellTransformation([[1, 0, 0], [0, 1, 0], [0, 0, 3]])
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filename_suffix = "90_approx"
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base_supercell = tf.apply_transformation(structure)
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print(f"已生成基准超胞，实际原子数: {len(base_supercell)}")
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li_indices = [i for i, site in enumerate(base_supercell.sites) if site.species_string == 'Li']
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y_indices = [i for i, site in enumerate(base_supercell.sites) if site.species_string == 'Y']
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if not li_indices or not y_indices:
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print("错误：在超胞中未找到足够的Li或Y原子来引入缺陷。")
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continue
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# 2. 循环生成多个独特的缺陷构型
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used_pairs = set()
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for i in range(num_configs):
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defect_supercell = base_supercell.copy()
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# 确保随机选择的交换对是全新的
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while True:
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li_swap_idx = random.choice(li_indices)
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y_swap_idx = random.choice(y_indices)
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# 使用排序后的元组作为键，确保(a,b)和(b,a)被视为相同
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pair = tuple(sorted((li_swap_idx, y_swap_idx)))
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if pair not in used_pairs:
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used_pairs.add(pair)
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break
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# 引入缺陷
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defect_supercell.replace(li_swap_idx, "Y")
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defect_supercell.replace(y_swap_idx, "Li")
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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})。")
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# 3. 保存为带编号的POSCAR文件
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poscar_filename = f"POSCAR_Pnma_{filename_suffix}_antisite_defect_{i}"
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poscar_path = os.path.join(output_path, poscar_filename)
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defect_supercell.to(fmt="poscar", filename=poscar_path)
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print(f" 已保存文件: {poscar_path}")
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if __name__ == '__main__':
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# 请将您的Pnma相CIF文件保存，并修改此路径
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# 这里我们使用您提供的参考文件名 'origin.cif'
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cif_file_path = "data/Pnma/origin.cif"
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output_directory = "raw/Pnma/output"
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create_multiple_pnma_supercells(cif_file_path, num_configs=3, output_path=output_directory)
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print("\nPnma相处理完成！") |