Structure-screening/str-predict.py at main · Chakra-phys/Structure-screening · GitHub

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# %%
import numpy as np
#import pandas as pd
from ase.io import read,write
#import json
from ase import Atoms
from chgnet.model.model import CHGNet
from pymatgen.core import Structure
#from pymatgen.io.ase import AseAtomsAdaptor
from icet import (ClusterSpace, StructureContainer,
                  CrossValidationEstimator, ClusterExpansion)
#from icet.tools import map_structure_to_reference, ConvexHull, enumerate_structures
from icet.tools.structure_generation import occupy_structure_randomly

# %%
#from icet import ClusterSpace

prim = read('FeCuS2.vasp')
prim_symbols = prim.get_chemical_symbols()

chemical_symbols = []
for s in prim_symbols:
    if s == 'Cu':
        # allow Li or vacancy on Li sites
        chemical_symbols.append(['Zn','Cu'])
        #chemical_symbols.append(['Li','Mn','Ti','X'])
        #chemical_symbols.append(['Li', 'X'])
#    elif s == 'Ti':
        # keep Ti fixed
#        chemical_symbols.append(['Li','Mn','X'])
#    elif s == 'O':
#        chemical_symbols.append(['O','F'])
    else:
        # fallback: keep whatever is present
        chemical_symbols.append([s])

print(chemical_symbols)   # sanity check: order should match prim.get_chemical_symbols()

cs = ClusterSpace(structure=prim,
                  cutoffs=[12.0, 6.0, 4.0],
                  chemical_symbols=chemical_symbols,
                  symprec=1e-05,
                  position_tolerance=0.5)

print(cs)   # should show active sublattices (A, B, ...) and list allowed species

# %%
supercell=prim #*[3,3,3]

# %%
target_concentration = {'A':{'Zn': 4/8, 'Cu':4/8}}#,'A':{'O':29/32,'F':3/32}}
chgnet = CHGNet.load()
f1=open("E_rand-chgnet.dat","a")
#element_to_remove = "X"
for i in range(1,5000):
    occupy_structure_randomly(supercell, cluster_space=cs, target_concentrations=target_concentration)
 #   filtered_supercell = supercell[[atom.symbol not in element_to_remove for atom in supercell]]
 #   write("str-rand/str-rand-vac-{}.vasp".format(i),filtered_supercell,sort=True)
    write("str-rand/str-rand-vac-{}.vasp".format(i),supercell,sort=True)
    print(i)
    structure2=Structure.from_file("str-rand/str-rand-vac-{}.vasp".format(i))
    #write("str-rand/str-rand-{}.vasp".format(i),supercell,sort=True)
    #print(i)
    #structure=Structure.from_file("str-rand/str-rand-{}.vasp".format(i))
    prediction = chgnet.predict_structure(structure2)
    for key, unit in [
    ("energy", "eV/atom"),
   # ("forces", "eV/A"),
   # ("stress", "GPa"),
    ("magmom", "mu_B"),
    ]:
        #print(f"CHGNet-predicted {key} ({unit}): {prediction[key[0]]}\n")
        f1.write(f"CHGNet-predicted str {i} {key} ({unit}): {prediction[key[0]]}\n")
f1.close()