import glob
import json
import os
import re
from monty.serialization import loadfn, dumpfn
from pymatgen.analysis.defects.generators import VacancyGenerator
from pymatgen.core.structure import Structure
from dpgen import dlog
from dpgen.auto_test.Property import Property
from dpgen.auto_test.refine import make_refine
from dpgen.auto_test.reproduce import make_repro
from dpgen.auto_test.reproduce import post_repro
import dpgen.auto_test.lib.abacus as abacus
import dpgen.generator.lib.abacus_scf as abacus_scf
[docs]class Vacancy(Property):
def __init__(self,
parameter,inter_param=None):
parameter['reproduce'] = parameter.get('reproduce', False)
self.reprod = parameter['reproduce']
if not self.reprod:
if not ('init_from_suffix' in parameter and 'output_suffix' in parameter):
default_supercell = [1, 1, 1]
parameter['supercell'] = parameter.get('supercell', default_supercell)
self.supercell = parameter['supercell']
parameter['cal_type'] = parameter.get('cal_type', 'relaxation')
self.cal_type = parameter['cal_type']
default_cal_setting = {"relax_pos": True,
"relax_shape": True,
"relax_vol": True}
if 'cal_setting' not in parameter:
parameter['cal_setting'] = default_cal_setting
else:
if "relax_pos" not in parameter['cal_setting']:
parameter['cal_setting']['relax_pos'] = default_cal_setting['relax_pos']
if "relax_shape" not in parameter['cal_setting']:
parameter['cal_setting']['relax_shape'] = default_cal_setting['relax_shape']
if "relax_vol" not in parameter['cal_setting']:
parameter['cal_setting']['relax_vol'] = default_cal_setting['relax_vol']
self.cal_setting = parameter['cal_setting']
else:
parameter['cal_type'] = 'static'
self.cal_type = parameter['cal_type']
default_cal_setting = {"relax_pos": False,
"relax_shape": False,
"relax_vol": False}
if 'cal_setting' not in parameter:
parameter['cal_setting'] = default_cal_setting
else:
if "relax_pos" not in parameter['cal_setting']:
parameter['cal_setting']['relax_pos'] = default_cal_setting['relax_pos']
if "relax_shape" not in parameter['cal_setting']:
parameter['cal_setting']['relax_shape'] = default_cal_setting['relax_shape']
if "relax_vol" not in parameter['cal_setting']:
parameter['cal_setting']['relax_vol'] = default_cal_setting['relax_vol']
self.cal_setting = parameter['cal_setting']
parameter['init_from_suffix'] = parameter.get('init_from_suffix', '00')
self.init_from_suffix = parameter['init_from_suffix']
self.parameter = parameter
self.inter_param = inter_param if inter_param != None else {'type': 'vasp'}
[docs] def make_confs(self,
path_to_work,
path_to_equi,
refine=False):
path_to_work = os.path.abspath(path_to_work)
if os.path.exists(path_to_work):
dlog.warning('%s already exists' % path_to_work)
else:
os.makedirs(path_to_work)
path_to_equi = os.path.abspath(path_to_equi)
if 'start_confs_path' in self.parameter and os.path.exists(self.parameter['start_confs_path']):
init_path_list = glob.glob(os.path.join(self.parameter['start_confs_path'], '*'))
struct_init_name_list = []
for ii in init_path_list:
struct_init_name_list.append(ii.split('/')[-1])
struct_output_name = path_to_work.split('/')[-2]
assert struct_output_name in struct_init_name_list
path_to_equi = os.path.abspath(os.path.join(self.parameter['start_confs_path'],
struct_output_name, 'relaxation', 'relax_task'))
task_list = []
cwd = os.getcwd()
if self.reprod:
print('vacancy reproduce starts')
if 'init_data_path' not in self.parameter:
raise RuntimeError("please provide the initial data path to reproduce")
init_data_path = os.path.abspath(self.parameter['init_data_path'])
task_list = make_repro(self.inter_param,init_data_path, self.init_from_suffix,
path_to_work, self.parameter.get('reprod_last_frame', False))
os.chdir(cwd)
else:
if refine:
print('vacancy refine starts')
task_list = make_refine(self.parameter['init_from_suffix'],
self.parameter['output_suffix'],
path_to_work)
init_from_path = re.sub(self.parameter['output_suffix'][::-1],
self.parameter['init_from_suffix'][::-1],
path_to_work[::-1], count=1)[::-1]
task_list_basename = list(map(os.path.basename, task_list))
for ii in task_list_basename:
init_from_task = os.path.join(init_from_path, ii)
output_task = os.path.join(path_to_work, ii)
os.chdir(output_task)
if os.path.isfile('supercell.json'):
os.remove('supercell.json')
if os.path.islink('supercell.json'):
os.remove('supercell.json')
os.symlink(os.path.relpath(os.path.join(init_from_task, 'supercell.json')), 'supercell.json')
os.chdir(cwd)
else:
if self.inter_param['type'] == 'abacus':
CONTCAR = abacus.final_stru(path_to_equi)
POSCAR = 'STRU'
else:
CONTCAR = 'CONTCAR'
POSCAR = 'POSCAR'
equi_contcar = os.path.join(path_to_equi, CONTCAR)
if not os.path.exists(equi_contcar):
raise RuntimeError("please do relaxation first")
if self.inter_param['type'] == 'abacus':
ss = abacus.stru2Structure(equi_contcar)
else:
ss = Structure.from_file(equi_contcar)
vds = VacancyGenerator(ss)
dss = []
for jj in vds:
dss.append(jj.generate_defect_structure(self.supercell))
print('gen vacancy with supercell ' + str(self.supercell))
os.chdir(path_to_work)
if os.path.isfile(POSCAR):
os.remove(POSCAR)
if os.path.islink(POSCAR):
os.remove(POSCAR)
os.symlink(os.path.relpath(equi_contcar), POSCAR)
# task_poscar = os.path.join(output, 'POSCAR')
for ii in range(len(dss)):
output_task = os.path.join(path_to_work, 'task.%06d' % ii)
os.makedirs(output_task, exist_ok=True)
os.chdir(output_task)
for jj in ['INCAR', 'POTCAR', 'POSCAR', 'conf.lmp', 'in.lammps','STRU']:
if os.path.exists(jj):
os.remove(jj)
task_list.append(output_task)
dss[ii].to('POSCAR', 'POSCAR')
if self.inter_param['type'] == 'abacus':
abacus.poscar2stru("POSCAR",self.inter_param,"STRU")
os.remove('POSCAR')
# np.savetxt('supercell.out', self.supercell, fmt='%d')
dumpfn(self.supercell, 'supercell.json')
os.chdir(cwd)
return task_list
[docs] def post_process(self, task_list):
pass
[docs] def task_type(self):
return self.parameter['type']
[docs] def task_param(self):
return self.parameter
def _compute_lower(self,
output_file,
all_tasks,
all_res):
output_file = os.path.abspath(output_file)
res_data = {}
ptr_data = os.path.dirname(output_file) + '\n'
if not self.reprod:
ptr_data += "Structure: \tVac_E(eV) E(eV) equi_E(eV)\n"
idid = -1
for ii in all_tasks:
idid += 1
structure_dir = os.path.basename(ii)
task_result = loadfn(all_res[idid])
natoms = task_result['atom_numbs'][0]
equi_path = os.path.abspath(os.path.join(os.path.dirname(output_file), '../relaxation/relax_task'))
equi_result = loadfn(os.path.join(equi_path, 'result.json'))
equi_epa = equi_result['energies'][-1] / equi_result['atom_numbs'][0]
evac = task_result['energies'][-1] - equi_epa * natoms
supercell_index = loadfn(os.path.join(ii, 'supercell.json'))
ptr_data += "%s: %7.3f %7.3f %7.3f \n" % (str(supercell_index) + '-' + structure_dir,
evac, task_result['energies'][-1], equi_epa * natoms)
res_data[str(supercell_index) + '-' + structure_dir] = [evac, task_result['energies'][-1],
equi_epa * natoms]
else:
if 'init_data_path' not in self.parameter:
raise RuntimeError("please provide the initial data path to reproduce")
init_data_path = os.path.abspath(self.parameter['init_data_path'])
res_data, ptr_data = post_repro(init_data_path, self.parameter['init_from_suffix'],
all_tasks, ptr_data, self.parameter.get('reprod_last_frame', False))
with open(output_file, 'w') as fp:
json.dump(res_data, fp, indent=4)
return res_data, ptr_data