151 lines
5.7 KiB
Python
151 lines
5.7 KiB
Python
import random
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from typing import List
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from pymatgen.core import Structure
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from pymatgen.io.vasp import Poscar
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def _is_close_frac(z, target, tol=2e-2):
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t = target % 1.0
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return min(abs(z - t), abs(z - (t + 1)), abs(z - (t - 1))) < tol
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def make_model3_poscar_from_cif(cif_path: str,
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out_poscar: str = "POSCAR_model3_supercell",
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seed: int = 42,
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tol: float = 2e-2):
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"""
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将 model3.cif 扩胞为 [[3,0,0],[2,4,0],[0,0,6]] 的2160原子超胞，并把部分占据位点(Y2=0.75, Y3=0.25, Li2=0.5)
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显式有序化后写出 POSCAR。
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"""
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random.seed(seed)
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# 1) 读取 CIF
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s = Structure.from_file(cif_path)
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# 2) 扩胞（a_s=3a0, b_s=2a0+4b0, c_s=6c0）[1]
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T = [[3, 0, 0],
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[2, 4, 0],
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[0, 0, 6]]
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s.make_supercell(T)
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# 3) 识别三类需取整的位点：Y2、Y3、Li2
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y2_idx: List[int] = []
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y3_idx: List[int] = []
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li2_idx: List[int] = []
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for i, site in enumerate(s.sites):
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# 兼容不同版本pymatgen
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try:
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el = site.species.elements[0].symbol
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except Exception:
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ss = site.species_string
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el = "Li" if ss.startswith("Li") else ("Y" if ss.startswith("Y") else ("Cl" if ss.startswith("Cl") else ss))
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z = site.frac_coords[2]
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if el == "Y":
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if _is_close_frac(z, 0.488, tol):
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y2_idx.append(i)
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elif _is_close_frac(z, -0.065, tol) or _is_close_frac(z, 0.935, tol):
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y3_idx.append(i)
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elif el == "Li":
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if _is_close_frac(z, 0.5, tol):
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li2_idx.append(i)
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def choose_keep(idxs, frac_keep):
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n = len(idxs)
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k = int(round(n * frac_keep))
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if k < 0: k = 0
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if k > n: k = n
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keep = set(random.sample(idxs, k)) if 0 < k < n else set(idxs if k == n else [])
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drop = [i for i in idxs if i not in keep]
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return keep, drop
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keep_y2, drop_y2 = choose_keep(y2_idx, 0.75)
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keep_y3, drop_y3 = choose_keep(y3_idx, 0.25)
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keep_li2, drop_li2 = choose_keep(li2_idx, 0.50)
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# 4) 保留者占据设为1，其余删除
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for i in keep_y2 | keep_y3:
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s.replace(i, "Y")
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for i in keep_li2:
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s.replace(i, "Li")
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to_remove = sorted(drop_y2 + drop_y3 + drop_li2, reverse=True)
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for i in to_remove:
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s.remove_sites([i])
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# 5) 最终清理：消除任何残留的部分占据（防止 POSCAR 写出报错）
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# 若有 site.is_ordered==False，则取该站位的“主要元素”替换为占据=1
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for i, site in enumerate(s.sites):
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if not site.is_ordered:
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d = site.species.as_dict() # {'Li': 0.5} 或 {'Li':0.5,'Y':0.5}
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elem = max(d.items(), key=lambda kv: kv[1])[0]
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s.replace(i, elem)
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# 6) 排序并写出 POSCAR
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order = {"Li": 0, "Y": 1, "Cl": 2}
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s = s.get_sorted_structure(key=lambda site: order.get(site.species.elements[0].symbol, 99))
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Poscar(s).write_file(out_poscar)
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# 报告
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comp = {k: int(v) for k, v in s.composition.as_dict().items()}
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print(f"写出 {out_poscar}；总原子数 = {len(s)}")
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print(f"Y2识别={len(y2_idx)}，Y3识别={len(y3_idx)}，Li2识别={len(li2_idx)}；组成={comp}")
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import random
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from typing import List
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from pymatgen.core import Structure
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from pymatgen.io.vasp import Poscar
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def make_pnma_poscar_from_cif(cif_path: str,
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out_poscar: str = "POSCAR_pnma_supercell",
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seed: int = 42,
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supercell=(3,3,6),
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tol: float = 1e-6):
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"""
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读取 Pnma 的 CIF（如 origin.cif），扩胞到 2160 原子，并把部分占据的 Li 位点(0.75)显式取整后写出 POSCAR。
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默认超胞尺度为(3,3,6)，体积放大因子=54，40原子/原胞×54=2160 [1][3]。
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"""
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random.seed(seed)
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s = Structure.from_file(cif_path)
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# 扩胞；Pnma原胞已是正交，直接用对角放缩
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s.make_supercell(supercell)
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# 找出所有“部分占据的 Li”位点
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partial_li_idx: List[int] = []
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for i, site in enumerate(s.sites):
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if not site.is_ordered:
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d = site.species.as_dict() # 例如 {'Li': 0.75}
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# 只处理主要元素是Li且占据<1的位点
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m_elem, m_occ = max(d.items(), key=lambda kv: kv[1])
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if m_elem == "Li" and m_occ < 1 - tol:
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partial_li_idx.append(i)
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# 以占据0.75进行随机取整：保留75%，其余删除为“空位”
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n = len(partial_li_idx)
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k = int(round(n * 0.75))
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keep = set(random.sample(partial_li_idx, k)) if 0 < k < n else set(partial_li_idx if k == n else [])
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drop = sorted([i for i in partial_li_idx if i not in keep], reverse=True)
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# 保留者设为占据=1；删除其余
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for i in keep:
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s.replace(i, "Li")
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for i in drop:
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s.remove_sites([i])
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# 兜底：若仍有部分占据，强制取主要元素
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for i, site in enumerate(s.sites):
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if not site.is_ordered:
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d = site.species.as_dict()
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elem = max(d.items(), key=lambda kv: kv[1])[0]
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s.replace(i, elem)
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# 排序并写POSCAR
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order = {"Li": 0, "Y": 1, "Cl": 2}
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s = s.get_sorted_structure(key=lambda site: order.get(site.species.elements[0].symbol, 99))
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Poscar(s).write_file(out_poscar)
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comp = {k: int(v) for k, v in s.composition.as_dict().items()}
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print(f"写出 {out_poscar}；总原子数 = {len(s)}；组成 = {comp}")
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if __name__=="__main__":
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# make_model3_poscar_from_cif("data/P3ma/model3.cif","data/P3ma/supercell_model4.poscar")
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make_pnma_poscar_from_cif("data/Pnma/origin.cif","data/Pnma/supercell_pnma.poscar",seed=42) |