一些小修改

This commit is contained in:
2025-11-19 12:23:17 +08:00
parent 95d719cc1e
commit 80ae03c8c1
25 changed files with 2291 additions and 17 deletions

View File

@@ -0,0 +1,115 @@
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_pnma_structure(disordered_structure):
"""
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。
"""
s = disordered_structure.copy()
# 根据origin.cif, Li位点的占位率为0.75 [5]
partial_li_indices = [i for i, site in enumerate(s.sites) if "Li" in site.species and not site.is_ordered]
# 根据0.75的占位率随机选择要保留的Li原子
num_to_keep = int(round(len(partial_li_indices) * 0.75))
keep_indices = set(random.sample(partial_li_indices, num_to_keep))
# 找出需要删除的原子索引
to_remove_indices = [i for i in partial_li_indices if i not in keep_indices]
s.remove_sites(sorted(to_remove_indices, reverse=True))
# 重新创建一个新的、完全有序的结构,避免任何副作用
ordered_species = []
ordered_coords = []
for site in s.sites:
# 只取每个位点的主要元素
main_specie = site.species.elements[0]
ordered_species.append(main_specie)
ordered_coords.append(site.frac_coords)
final_structure = mg.Structure(s.lattice, ordered_species, ordered_coords)
return final_structure
def create_multiple_pnma_supercells(cif_path, num_configs=3, output_path="."):
"""
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。
"""
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Pnma结构...")
parser = CifParser(cif_path)
disordered_structure = parser.parse_structures(primitive=False)[0]
structure = create_ordered_pnma_structure(disordered_structure)
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。")
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
while True:
li_swap_idx = random.choice(li_indices)
y_swap_idx = random.choice(y_indices)
# 使用排序后的元组作为键,确保(a,b)和(b,a)被视为相同
pair = tuple(sorted((li_swap_idx, y_swap_idx)))
if pair not in used_pairs:
used_pairs.add(pair)
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_Pnma_{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__':
# 请将您的Pnma相CIF文件保存并修改此路径
# 这里我们使用您提供的参考文件名 'origin.cif'
cif_file_path = "data/Pnma/origin.cif"
output_directory = "raw/Pnma/output"
create_multiple_pnma_supercells(cif_file_path, num_configs=3, output_path=output_directory)
print("\nPnma相处理完成")