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Warren-Cowley-Parameter.py
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Warren-Cowley-Parameter.py
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'''
计算高熵合金的Warren-Cowley参数
输入：POSCAR、目标原子对
'''
import numpy as np
# 读取POSCAR文件并处理数据
def read_POSCAR(POSCAR):
with open(POSCAR, 'r', encoding='utf-8') as f:
POSCAR = f.readlines()
elements = POSCAR[5].strip().split() # 元素名称
element_number = POSCAR[6].strip().split() #
element_number = [int(x) for x in element_number] # 每种元素个数
elements = dict(zip(elements, element_number))
# element_list = [] # 储存每个原子
# for i in range(len(elements)):
# for j in range(element_number[i]):
# element_list.append(elements[i] + str(j+1))
# 读取坐标信息
if POSCAR[7].strip() == "Selective dynamics":
coordiate = POSCAR[9:]
else:
coordiate = POSCAR[8:]
coor = [] # 储存每个原子的坐标
for i in coordiate:
one_coor = i.strip().split()[:3]
if one_coor:
one_coor = [float(x) for x in one_coor]
coor.append(one_coor)
# 生成每种元素对应的坐标字典
elements_coor = [] # 每种元素的坐标为一个小列表，储存在一个大列表中，最后合并为一个大字典
index = 0
for i in element_number:
one_element_coor = coor[index:index+i]
index += i
elements_coor.append(one_element_coor)
atoms = dict(zip(elements, elements_coor))
return atoms, elements, coor
# 计算两点之间的距离，考虑周期性边界条件
def distance(i, j, scalor):
dx = (i[0] - j[0])*scalor
if (abs(dx) > scalor*0.5):
dx = scalor - abs(dx)
dy = (i[1] - j[1])*scalor
if (abs(dy) > scalor*0.5):
dy = scalor - abs(dy)
dz = (i[2] - j[2])*scalor
if (abs(dz) > scalor*0.5):
dz = scalor - abs(dz)
dis = np.sqrt(dx**2 + dy**2 + dz**2)
return dis
# 计算第一近邻距离，只需将第一个原子与剩余原子的距离算一遍，取最小值即可
def first_neigh(coor, scalor):
first = coor[0]
distance_list = []
for i in coor[1:]:
dis = distance(i, first, scalor=12)
distance_list.append(dis)
first_neighboor = min(distance_list)
return first_neighboor
# 计算warren-cowley参数，一次性计算出所有原子对的SRO参数
def cacu_SRO(elements, first_neighboor):
SRO = [] # 储存计算出的SRO参数，是按顺序排列的，之后转换为n*n矩阵即可
for element_i in elements:
Zi = 0
for element_k in elements:
for i in atoms[element_i]:
for j in atoms[element_k]:
if distance(i, j, scalor=12) == first_neighboor:
Zi += 1
for element_j in elements:
Zij = 0
for i in atoms[element_i]:
for j in atoms[element_j]:
if distance(i, j, scalor=12) == first_neighboor:
# i原子对应的j近邻原子数加1
Zij += 1
# 计算该原子对的SRO，需要该原子数，原子分数
fraction_j = elements[element_j] / sum(elements.values())
WCP_ij = 1 - Zij / (Zi * fraction_j)
SRO.append(WCP_ij)
# print(Zij, Zi, fraction_j)
return np.array(SRO).reshape(4, 4)
atoms, elements, coor = read_POSCAR('POSCAR-5')
first_neighboor = first_neigh(coor, scalor=12)
SRO = cacu_SRO(elements, first_neighboor)
print(SRO)
# print(first_neighboor)
# print(atoms)
# print(elements)
atoms, elements, coor = read_POSCAR('best1-POSCAR')
first_neighboor = first_neigh(coor, scalor=12)
SRO = cacu_SRO(elements, first_neighboor)
print(SRO)
atoms, elements, coor = read_POSCAR('best2-POSCAR')
first_neighboor = first_neigh(coor, scalor=12)
SRO = cacu_SRO(elements, first_neighboor)
print(SRO)