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)
cwd = os.getcwd()
task_list = []
if refine:
task_list = make_refine(
self.parameter['init_from_suffix'],
self.parameter['output_suffix'], path_to_work,
(self.vol_end - self.vol_start) / self.vol_step)
os.chdir(cwd)
if self.reprod:
if 'vasp_path' not in self.parameter:
raise RuntimeError(
"please provide the vasp_path for reproduction")
vasp_path = os.path.abspath(self.parameter['vasp_path'])
task_list = reproduce.make_repro(vasp_path, path_to_work)
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")
vol_to_poscar = vasp.poscar_vol(equi_contcar) / vasp.poscar_natoms(
equi_contcar)
for vol in np.arange(self.vol_start, self.vol_end, self.vol_step):
task_num = (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.)
self.parameter['scale2equi'] = scale # 06/14
vasp.poscar_scale('POSCAR.orig', 'POSCAR', scale)
os.chdir(cwd)
return task_list
def make_deepmd_lammps (jdata, conf_dir) :
deepmd_model_dir = jdata['deepmd_model_dir']
deepmd_type_map = jdata['deepmd_type_map']
ntypes = len(deepmd_type_map)
deepmd_model_dir = os.path.abspath(deepmd_model_dir)
deepmd_models = glob.glob(os.path.join(deepmd_model_dir, '*pb'))
deepmd_models_name = [os.path.basename(ii) for ii in deepmd_models]
vol_start = jdata['vol_start']
vol_end = jdata['vol_end']
vol_step = jdata['vol_step']
# # get equi props
# equi_path = re.sub('confs', global_equi_name, conf_path)
# equi_path = os.path.join(equi_path, 'lmp')
# equi_log = os.path.join(equi_path, 'log.lammps')
# if not os.path.isfile(equi_log) :
# raise RuntimeError("the system should be equilibriated first")
# natoms, epa, vpa = lammps.get_nev(equi_log)
# task path
task_path = re.sub('confs', global_task_name, conf_dir)
task_path = os.path.abspath(task_path)
os.makedirs(task_path, exist_ok = True)
cwd = os.getcwd()
conf_path = os.path.abspath(conf_dir)
from_poscar = os.path.join(conf_path, 'POSCAR')
to_poscar = os.path.join(task_path, 'POSCAR')
if os.path.exists(to_poscar) :
assert(filecmp.cmp(from_poscar, to_poscar))
else :
os.chdir(task_path)
os.symlink(os.path.relpath(from_poscar), 'POSCAR')
os.chdir(cwd)
volume = vasp.poscar_vol(to_poscar)
natoms = vasp.poscar_natoms(to_poscar)
vpa = volume / natoms
# structrure
ss = Structure.from_file(to_poscar)
# lmp path
lmp_path = os.path.join(task_path, 'deepmd')
os.makedirs(lmp_path, exist_ok = True)
# # lmp conf
# conf_file = os.path.join(lmp_path, 'conf.lmp')
# lammps.cvt_lammps_conf(to_poscar, conf_file)
# ptypes = vasp.get_poscar_types(to_poscar)
# lammps.apply_type_map(conf_file, deepmd_type_map, ptypes)
os.chdir(lmp_path)
for ii in deepmd_models_name :
if os.path.exists(ii) :
os.remove(ii)
for (ii,jj) in zip(deepmd_models, deepmd_models_name) :
os.symlink(os.path.relpath(ii), jj)
share_models = glob.glob(os.path.join(lmp_path, '*pb'))
for vol in np.arange(vol_start, vol_end, vol_step) :
vol_path = os.path.join(lmp_path, 'vol-%.2f' % vol)
print('# generate %s' % (vol_path))
os.makedirs(vol_path, exist_ok = True)
os.chdir(vol_path)
#print(vol_path)
for ii in ['conf.lmp', 'conf.lmp'] + deepmd_models_name :
if os.path.exists(ii) :
os.remove(ii)
# # link conf
# os.symlink(os.path.relpath(conf_file), 'conf.lmp')
# make conf
scale_ss = ss.copy()
scale_ss.scale_lattice(vol * natoms)
scale_ss.to('POSCAR', 'POSCAR')
lammps.cvt_lammps_conf('POSCAR', 'conf.lmp')
ptypes = vasp.get_poscar_types('POSCAR')
lammps.apply_type_map('conf.lmp', deepmd_type_map, ptypes)
# link models
for (ii,jj) in zip(share_models, deepmd_models_name) :
os.symlink(os.path.relpath(ii), jj)
# make lammps input
scale = (vol / vpa) ** (1./3.)
fc = lammps.make_lammps_press_relax('conf.lmp', ntypes, scale,lammps.inter_deepmd, deepmd_models_name)
with open(os.path.join(vol_path, 'lammps.in'), 'w') as fp :
fp.write(fc)
os.chdir(cwd)
def make_meam_lammps_fixv (jdata, conf_dir) :
meam_potfile_dir = jdata['meam_potfile_dir']
meam_potfile_dir = os.path.abspath(meam_potfile_dir)
meam_potfile = jdata['meam_potfile']
meam_potfile = [os.path.join(meam_potfile_dir,ii) for ii in meam_potfile]
meam_potfile_name = jdata['meam_potfile']
type_map = jdata['meam_type_map']
ntypes = len(type_map)
meam_param = {'meam_potfile' : jdata['meam_potfile'],
'meam_type': jdata['meam_param_type']}
vol_start = jdata['vol_start']
vol_end = jdata['vol_end']
vol_step = jdata['vol_step']
# get equi props
equi_path = re.sub('confs', global_equi_name, conf_dir)
task_path = re.sub('confs', global_task_name, conf_dir)
equi_path = os.path.join(equi_path, 'meam')
task_path = os.path.join(task_path, 'meam')
equi_path = os.path.abspath(equi_path)
task_path = os.path.abspath(task_path)
equi_log = os.path.join(equi_path, 'log.lammps')
equi_dump = os.path.join(equi_path, 'dump.relax')
os.makedirs(task_path, exist_ok = True)
task_poscar = os.path.join(task_path, 'POSCAR')
lammps.poscar_from_last_dump(equi_dump, task_poscar, type_map)
cwd = os.getcwd()
volume = vasp.poscar_vol(task_poscar)
natoms = vasp.poscar_natoms(task_poscar)
vpa = volume / natoms
# structrure
ss = Structure.from_file(task_poscar)
# make lammps.in
fc = lammps.make_lammps_equi('conf.lmp',
ntypes,
lammps.inter_meam,
meam_param,
change_box = False)
f_lammps_in = os.path.join(task_path, 'lammps.in')
with open(f_lammps_in, 'w') as fp :
fp.write(fc)
# make vols
for vol in np.arange(vol_start, vol_end, vol_step) :
vol_path = os.path.join(task_path, 'vol-%.2f' % vol)
print('# generate %s' % (vol_path))
os.makedirs(vol_path, exist_ok = True)
os.chdir(vol_path)
for ii in ['conf.lmp', 'conf.lmp', 'lammps.in'] + meam_potfile_name :
if os.path.exists(ii) :
os.remove(ii)
# make conf
scale_ss = ss.copy()
scale_ss.scale_lattice(vol * natoms)
scale_ss.to('POSCAR', 'POSCAR')
lammps.cvt_lammps_conf('POSCAR', 'conf.lmp')
ptypes = vasp.get_poscar_types('POSCAR')
lammps.apply_type_map('conf.lmp', type_map, ptypes)
# link lammps.in
os.symlink(os.path.relpath(f_lammps_in), 'lammps.in')
# link models
for (ii,jj) in zip(meam_potfile, meam_potfile_name) :
os.symlink(os.path.relpath(ii), jj)
# make lammps input
os.chdir(cwd)
def make_vasp(jdata, conf_dir) :
fp_params = jdata['vasp_params']
ecut = fp_params['ecut']
ediff = fp_params['ediff']
npar = fp_params['npar']
kpar = fp_params['kpar']
kspacing = fp_params['kspacing']
kgamma = fp_params['kgamma']
vol_start = jdata['vol_start']
vol_end = jdata['vol_end']
vol_step = jdata['vol_step']
conf_path = os.path.abspath(conf_dir)
task_path = re.sub('confs', global_task_name, conf_path)
os.makedirs(task_path, exist_ok = True)
cwd = os.getcwd()
from_poscar = os.path.join(conf_path, 'POSCAR')
to_poscar = os.path.join(task_path, 'POSCAR')
if os.path.exists(to_poscar) :
assert(filecmp.cmp(from_poscar, to_poscar))
else :
os.chdir(task_path)
os.symlink(os.path.relpath(from_poscar), 'POSCAR')
os.chdir(cwd)
vol_to_poscar = vasp.poscar_vol(to_poscar) / vasp.poscar_natoms(to_poscar)
# print(to_poscar, vol_to_poscar)
is_alloy = \
os.path.exists(
os.path.join(
os.path.join(conf_path, '..'),
'alloy'
)
)
vasp_path = os.path.join(task_path, 'vasp-k%.2f' % kspacing)
os.makedirs(vasp_path, exist_ok = True)
os.chdir(vasp_path)
# gen incar
if is_alloy :
fc = vasp.make_vasp_relax_incar(ecut, ediff, True, True, False, npar, kpar, kspacing, kgamma)
else :
fc = vasp.make_vasp_relax_incar(ecut, ediff, False, True, False, npar, kpar, kspacing, kgamma)
with open('INCAR', 'w') as fp :
fp.write(fc)
# gen potcar
with open(to_poscar,'r') as fp :
lines = fp.read().split('\n')
ele_list = lines[5].split()
potcar_map = jdata['potcar_map']
potcar_list = []
for ii in ele_list :
assert(os.path.exists(potcar_map[ii]))
potcar_list.append(potcar_map[ii])
with open('POTCAR', 'w') as outfile:
for fname in potcar_list:
with open(fname) as infile:
outfile.write(infile.read())
# loop over volumes
for vol in np.arange(vol_start, vol_end, vol_step) :
vol_path = os.path.join(vasp_path, 'vol-%.2f' % vol)
os.makedirs(vol_path, exist_ok = True)
os.chdir(vol_path)
print(vol_path)
for ii in ['INCAR', 'POTCAR', 'POSCAR.orig', 'POSCAR'] :
if os.path.exists(ii) :
os.remove(ii)
# link incar, potcar
os.symlink(os.path.relpath(os.path.join(vasp_path, 'INCAR')), 'INCAR')
os.symlink(os.path.relpath(os.path.join(vasp_path, 'POTCAR')), 'POTCAR')
# gen poscar
os.symlink(os.path.relpath(to_poscar), 'POSCAR.orig')
scale = (vol / vol_to_poscar) ** (1./3.)
# print(scale)
vasp.poscar_scale('POSCAR.orig', 'POSCAR', scale)
# print(vol_path, vasp.poscar_vol('POSCAR') / vasp.poscar_natoms('POSCAR'))
os.chdir(cwd)
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_vasp(jdata, conf_dir) :
vol_start = jdata['vol_start']
vol_end = jdata['vol_end']
vol_step = jdata['vol_step']
eos_relax_cell_shape = jdata.get('eos_relax_cell_shape', True)
conf_path = os.path.abspath(conf_dir)
conf_poscar = os.path.join(conf_path, 'POSCAR')
if 'relax_incar' in jdata.keys():
vasp_str='vasp-relax_incar'
else:
kspacing = jdata['vasp_params']['kspacing']
vasp_str='vasp-k%.2f' % kspacing
# get equi poscar
equi_path = re.sub('confs', global_equi_name, conf_path)
equi_path = os.path.join(equi_path, vasp_str)
equi_contcar = os.path.join(equi_path, 'CONTCAR')
task_path = re.sub('confs', global_task_name, conf_path)
task_path = os.path.join(task_path, vasp_str)
os.makedirs(task_path, exist_ok = True)
# link poscar
cwd = os.getcwd()
from_poscar = os.path.join(equi_contcar)
to_poscar = os.path.join(task_path, 'POSCAR')
if os.path.exists(to_poscar) :
assert(filecmp.cmp(from_poscar, to_poscar))
else :
os.chdir(task_path)
os.symlink(os.path.relpath(from_poscar), 'POSCAR')
os.chdir(cwd)
vol_to_poscar = vasp.poscar_vol(to_poscar) / vasp.poscar_natoms(to_poscar)
# print(to_poscar, vol_to_poscar)
is_alloy = \
os.path.exists(
os.path.join(
os.path.join(conf_path, '..'),
'alloy'
)
)
# read potcar
with open(to_poscar,'r') as fp :
lines = fp.read().split('\n')
ele_list = lines[5].split()
potcar_map = jdata['potcar_map']
potcar_list = []
for ii in ele_list :
assert os.path.exists(os.path.abspath(potcar_map[ii])),"No POTCAR in the potcar_map of %s"%(ii)
potcar_list.append(os.path.abspath(potcar_map[ii]))
# gen incar
if 'relax_incar' in jdata.keys():
relax_incar_path = jdata['relax_incar']
assert(os.path.exists(relax_incar_path))
relax_incar_path = os.path.abspath(relax_incar_path)
incar = incar_upper(Incar.from_file(relax_incar_path))
if eos_relax_cell_shape:
isif = 4
else:
isif = 2
if incar.get('ISIF') != isif:
dlog.info("%s:%s setting ISIF to %d" % (__file__, make_vasp.__name__, isif))
incar['ISIF'] = isif
fc = incar.get_string()
kspacing = incar['KSPACING']
kgamma = incar['KGAMMA']
else :
fp_params = jdata['vasp_params']
ecut = fp_params['ecut']
ediff = fp_params['ediff']
npar = fp_params['npar']
kpar = fp_params['kpar']
kspacing = fp_params['kspacing']
kgamma = fp_params['kgamma']
fc = vasp.make_vasp_relax_incar(ecut, ediff, is_alloy, True, False, npar, kpar, kspacing, kgamma)
os.chdir(task_path)
with open('INCAR', 'w') as fp :
fp.write(fc)
# gen potcar
with open('POTCAR', 'w') as outfile:
for fname in potcar_list:
with open(fname) as infile:
outfile.write(infile.read())
# loop over volumes
for vol in np.arange(vol_start, vol_end, vol_step) :
vol_path = os.path.join(task_path, 'vol-%.2f' % vol)
os.makedirs(vol_path, exist_ok = True)
os.chdir(vol_path)
for ii in ['INCAR', 'POTCAR', 'POSCAR.orig', 'POSCAR'] :
if os.path.exists(ii) :
os.remove(ii)
# link incar, potcar
os.symlink(os.path.relpath(os.path.join(task_path, 'INCAR')), 'INCAR')
os.symlink(os.path.relpath(os.path.join(task_path, 'POTCAR')), 'POTCAR')
# gen poscar
os.symlink(os.path.relpath(to_poscar), 'POSCAR.orig')
scale = (vol / vol_to_poscar) ** (1./3.)
# print(scale)
vasp.poscar_scale('POSCAR.orig', 'POSCAR', scale)
# print(vol_path, vasp.poscar_vol('POSCAR') / vasp.poscar_natoms('POSCAR'))
# gen kpoints
fc = vasp.make_kspacing_kpoints('POSCAR', kspacing, kgamma)
with open('KPOINTS', 'w') as fp: fp.write(fc)
#copy cvasp
if ('cvasp' in jdata) and (jdata['cvasp'] == True):
shutil.copyfile(cvasp_file, os.path.join(vol_path,'cvasp.py'))
os.chdir(cwd)
def make_lammps_fixv (jdata, conf_dir,task_type) :
fp_params = jdata['lammps_params']
model_dir = fp_params['model_dir']
type_map = fp_params['type_map']
model_dir = os.path.abspath(model_dir)
model_name =fp_params['model_name']
deepmd_version = fp_params.get("deepmd_version", "0.12")
if not model_name and task_type =='deepmd':
models = glob.glob(os.path.join(model_dir, '*pb'))
model_name = [os.path.basename(ii) for ii in models]
else:
models = [os.path.join(model_dir,ii) for ii in model_name]
model_param = {'model_name' : model_name,
'param_type': fp_params['model_param_type'],
'deepmd_version' : deepmd_version}
ntypes = len(type_map)
vol_start = jdata['vol_start']
vol_end = jdata['vol_end']
vol_step = jdata['vol_step']
# get equi props
equi_path = re.sub('confs', global_equi_name, conf_dir)
task_path = re.sub('confs', global_task_name, conf_dir)
equi_path = os.path.join(equi_path, task_type)
task_path = os.path.join(task_path, task_type)
equi_path = os.path.abspath(equi_path)
task_path = os.path.abspath(task_path)
equi_dump = os.path.join(equi_path, 'dump.relax')
os.makedirs(task_path, exist_ok = True)
task_poscar = os.path.join(task_path, 'POSCAR')
lammps.poscar_from_last_dump(equi_dump, task_poscar, type_map)
cwd = os.getcwd()
volume = vasp.poscar_vol(task_poscar)
natoms = vasp.poscar_natoms(task_poscar)
vpa = volume / natoms
# structrure
ss = Structure.from_file(task_poscar)
# make lammps.in
if task_type=='deepmd':
fc = lammps.make_lammps_equi('conf.lmp',
ntypes,
lammps.inter_deepmd,
model_param,
change_box = False)
elif task_type=='meam':
fc = lammps.make_lammps_equi('conf.lmp',
ntypes,
lammps.inter_meam,
model_param,
change_box = False)
f_lammps_in = os.path.join(task_path, 'lammps.in')
with open(f_lammps_in, 'w') as fp :
fp.write(fc)
os.chdir(task_path)
for ii in model_name :
if os.path.exists(ii) :
os.remove(ii)
for (ii,jj) in zip(models, model_name) :
os.symlink(os.path.relpath(ii), jj)
share_models = [os.path.join(task_path,ii) for ii in model_name]
# make vols
for vol in np.arange(vol_start, vol_end, vol_step) :
vol_path = os.path.join(task_path, 'vol-%.2f' % vol)
print('# generate %s' % (vol_path))
os.makedirs(vol_path, exist_ok = True)
os.chdir(vol_path)
for ii in ['conf.lmp', 'conf.lmp', 'lammps.in'] + model_name :
if os.path.exists(ii) :
os.remove(ii)
# make conf
scale_ss = ss.copy()
scale_ss.scale_lattice(vol * natoms)
scale_ss.to('POSCAR', 'POSCAR')
lammps.cvt_lammps_conf('POSCAR', 'conf.lmp')
ptypes = vasp.get_poscar_types('POSCAR')
lammps.apply_type_map('conf.lmp', type_map, ptypes)
# link lammps.in
os.symlink(os.path.relpath(f_lammps_in), 'lammps.in')
# link models
for (ii,jj) in zip(share_models,model_name) :
os.symlink(os.path.relpath(ii), jj)
# make lammps input
os.chdir(cwd)