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()
equi_contcar = os.path.join(path_to_equi, 'CONTCAR')
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')
# stress, deal with unsupported stress in dpdata
#with open(os.path.join(path_to_equi, 'result.json')) as fin:
# equi_result = json.load(fin)
#equi_stress = np.array(equi_result['stress']['data'])[-1]
equi_result = loadfn(os.path.join(path_to_equi, 'result.json'))
equi_stress = equi_result['stress'][-1]
dumpfn(equi_stress, 'equi.stress.json', indent=4)
os.chdir(cwd)
if refine:
print('elastic refine starts')
task_list = make_refine(self.parameter['init_from_suffix'],
self.parameter['output_suffix'],
path_to_work)
# record strain
# df = Strain.from_deformation(dfm_ss.deformations[idid])
# dumpfn(df.as_dict(), 'strain.json', indent=4)
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('strain.json'):
os.remove('strain.json')
copyfile(os.path.join(init_from_task, 'strain.json'),
'strain.json')
#os.symlink(os.path.relpath(
# os.path.join((re.sub(self.parameter['output_suffix'], self.parameter['init_from_suffix'], ii)),
# 'strain.json')),
# 'strain.json')
os.chdir(cwd)
else:
norm_def = self.norm_deform
shear_def = self.shear_deform
norm_strains = [
-norm_def, -0.5 * norm_def, 0.5 * norm_def, norm_def
]
shear_strains = [
-shear_def, -0.5 * shear_def, 0.5 * shear_def, shear_def
]
if not os.path.exists(equi_contcar):
raise RuntimeError("please do relaxation first")
ss = Structure.from_file(equi_contcar)
dfm_ss = DeformedStructureSet(ss,
symmetry=False,
norm_strains=norm_strains,
shear_strains=shear_strains)
n_dfm = len(dfm_ss)
print('gen with norm ' + str(norm_strains))
print('gen with shear ' + str(shear_strains))
for ii in range(n_dfm):
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'
]:
if os.path.exists(jj):
os.remove(jj)
task_list.append(output_task)
dfm_ss.deformed_structures[ii].to('POSCAR', 'POSCAR')
# record strain
df = Strain.from_deformation(dfm_ss.deformations[ii])
dumpfn(df.as_dict(), 'strain.json', indent=4)
os.chdir(cwd)
return task_list
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'))
cwd = os.getcwd()
task_list = []
if self.reprod:
print('eos 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(
init_data_path, self.init_from_suffix, path_to_work,
self.parameter.get('reprod_last_frame', True))
os.chdir(cwd)
else:
if refine:
print('eos refine starts')
task_list = make_refine(self.parameter['init_from_suffix'],
self.parameter['output_suffix'],
path_to_work)
os.chdir(cwd)
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('eos.json'):
os.remove('eos.json')
if os.path.islink('eos.json'):
os.remove('eos.json')
os.symlink(
os.path.relpath(
os.path.join(init_from_task, 'eos.json')),
'eos.json')
os.chdir(cwd)
else:
print('gen eos from ' + str(self.vol_start) + ' to ' +
str(self.vol_end) + ' by every ' + str(self.vol_step))
equi_contcar = os.path.join(path_to_equi, 'CONTCAR')
if not os.path.exists(equi_contcar):
raise RuntimeError("please do relaxation first")
vol_to_poscar = vasp.poscar_vol(
equi_contcar) / vasp.poscar_natoms(equi_contcar)
self.parameter['scale2equi'] = []
task_num = 0
while self.vol_start + self.vol_step * task_num < self.vol_end:
# for vol in np.arange(int(self.vol_start * 100), int(self.vol_end * 100), int(self.vol_step * 100)):
# vol = vol / 100.0
vol = self.vol_start + task_num * self.vol_step
#task_num = int((vol - self.vol_start) / self.vol_step)
output_task = os.path.join(path_to_work,
'task.%06d' % task_num)
os.makedirs(output_task, exist_ok=True)
os.chdir(output_task)
for ii in [
'INCAR', 'POTCAR', 'POSCAR.orig', 'POSCAR',
'conf.lmp', 'in.lammps'
]:
if os.path.exists(ii):
os.remove(ii)
task_list.append(output_task)
os.symlink(os.path.relpath(equi_contcar), 'POSCAR.orig')
# scale = (vol / vol_to_poscar) ** (1. / 3.)
scale = vol**(1. / 3.)
eos_params = {
'volume': vol * vol_to_poscar,
'scale': scale
}
dumpfn(eos_params, 'eos.json', indent=4)
self.parameter['scale2equi'].append(scale) # 06/22
vasp.poscar_scale('POSCAR.orig', 'POSCAR', scale)
task_num += 1
os.chdir(cwd)
return task_list
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(
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:
equi_contcar = os.path.join(path_to_equi, 'CONTCAR')
if not os.path.exists(equi_contcar):
raise RuntimeError("please do relaxation first")
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'
]:
if os.path.exists(jj):
os.remove(jj)
task_list.append(output_task)
dss[ii].to('POSCAR', 'POSCAR')
# np.savetxt('supercell.out', self.supercell, fmt='%d')
dumpfn(self.supercell, 'supercell.json')
os.chdir(cwd)
return task_list
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('surface 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(init_data_path, self.init_from_suffix,
path_to_work, self.parameter.get('reprod_last_frame', True))
os.chdir(cwd)
else:
if refine:
print('surface refine starts')
task_list = make_refine(self.parameter['init_from_suffix'],
self.parameter['output_suffix'],
path_to_work)
os.chdir(cwd)
# record miller
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('miller.json'):
os.remove('miller.json')
if os.path.islink('miller.json'):
os.remove('miller.json')
os.symlink(os.path.relpath(os.path.join(init_from_task, 'miller.json')), 'miller.json')
os.chdir(cwd)
else:
equi_contcar = os.path.join(path_to_equi, 'CONTCAR')
if not os.path.exists(equi_contcar):
raise RuntimeError("please do relaxation first")
ptypes = vasp.get_poscar_types(equi_contcar)
# gen structure
ss = Structure.from_file(equi_contcar)
# gen slabs
all_slabs = generate_all_slabs(ss, self.miller, self.min_slab_size, self.min_vacuum_size)
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(all_slabs)):
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']:
if os.path.exists(jj):
os.remove(jj)
task_list.append(output_task)
print("# %03d generate " % ii, output_task, " \t %d atoms" % len(all_slabs[ii].sites))
# make confs
all_slabs[ii].to('POSCAR', 'POSCAR.tmp')
vasp.regulate_poscar('POSCAR.tmp', 'POSCAR')
vasp.sort_poscar('POSCAR', 'POSCAR', ptypes)
vasp.perturb_xz('POSCAR', 'POSCAR', self.pert_xz)
# record miller
dumpfn(all_slabs[ii].miller_index, 'miller.json')
os.chdir(cwd)
return task_list
def make_confs(self, path_to_work, path_to_equi, refine=False):
path_to_work = os.path.abspath(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('interstitial 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(
init_data_path, self.init_from_suffix, path_to_work,
self.parameter.get('reprod_last_frame', False))
os.chdir(cwd)
else:
if refine:
print('interstitial 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))
os.chdir(path_to_work)
if os.path.isfile('element.out'):
os.remove('element.out')
if os.path.islink('element.out'):
os.remove('element.out')
os.symlink(
os.path.relpath(os.path.join(init_from_path,
'element.out')),
'element.out')
os.chdir(cwd)
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:
equi_contcar = os.path.join(path_to_equi, 'CONTCAR')
if not os.path.exists(equi_contcar):
raise RuntimeError("please do relaxation first")
ss = Structure.from_file(equi_contcar)
# gen defects
dss = []
insert_element_task = os.path.join(path_to_work, 'element.out')
if os.path.isfile(insert_element_task):
os.remove(insert_element_task)
for ii in self.insert_ele:
vds = InterstitialGenerator(ss, ii)
for jj in vds:
temp = jj.generate_defect_structure(self.supercell)
smallest_distance = list(
set(temp.distance_matrix.ravel()))[1]
if 'conf_filters' in self.parameter and 'min_dist' in self.parameter[
'conf_filters']:
min_dist = self.parameter['conf_filters'][
'min_dist']
if smallest_distance >= min_dist:
dss.append(temp)
with open(insert_element_task, 'a+') as fout:
print(ii, file=fout)
else:
dss.append(temp)
with open(insert_element_task, 'a+') as fout:
print(ii, file=fout)
# dss.append(jj.generate_defect_structure(self.supercell))
print('gen interstitial with supercell ' +
str(self.supercell) + ' with element ' +
str(self.insert_ele))
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'
]:
if os.path.exists(jj):
os.remove(jj)
task_list.append(output_task)
dss[ii].to('POSCAR', 'POSCAR')
# np.savetxt('supercell.out', self.supercell, fmt='%d')
dumpfn(self.supercell, 'supercell.json')
os.chdir(cwd)
return task_list