def make_lammps(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]
assert len(model_name) > 0, "No deepmd model in the model_dir"
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)
conf_path = os.path.abspath(conf_dir)
equi_path = re.sub('confs', global_task_name, conf_path)
os.makedirs(equi_path, exist_ok=True)
cwd = os.getcwd()
from_poscar = os.path.join(conf_path, 'POSCAR')
to_poscar = os.path.join(equi_path, 'POSCAR')
if os.path.exists(to_poscar):
assert (filecmp.cmp(from_poscar, to_poscar))
else:
os.chdir(equi_path)
os.symlink(os.path.relpath(from_poscar), 'POSCAR')
os.chdir(cwd)
# lmp path
lmp_path = os.path.join(equi_path, task_type)
os.makedirs(lmp_path, exist_ok=True)
print(lmp_path)
# lmp conf
conf_file = os.path.join(lmp_path, 'conf.lmp')
lammps.cvt_lammps_conf(to_poscar, os.path.relpath(conf_file))
ptypes = vasp.get_poscar_types(to_poscar)
lammps.apply_type_map(conf_file, type_map, ptypes)
# lmp input
if task_type == 'deepmd':
fc = lammps.make_lammps_equi(os.path.basename(conf_file), ntypes,
lammps.inter_deepmd, model_param)
elif task_type == 'meam':
fc = lammps.make_lammps_equi(os.path.basename(conf_file), ntypes,
lammps.inter_meam, model_param)
with open(os.path.join(lmp_path, 'lammps.in'), 'w') as fp:
fp.write(fc)
# link models
os.chdir(lmp_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)
os.chdir(cwd)
def make_meam_lammps(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']
}
conf_path = os.path.abspath(conf_dir)
equi_path = re.sub('confs', global_task_name, conf_path)
os.makedirs(equi_path, exist_ok=True)
cwd = os.getcwd()
from_poscar = os.path.join(conf_path, 'POSCAR')
to_poscar = os.path.join(equi_path, 'POSCAR')
if os.path.exists(to_poscar):
assert (filecmp.cmp(from_poscar, to_poscar))
else:
os.chdir(equi_path)
os.symlink(os.path.relpath(from_poscar), 'POSCAR')
os.chdir(cwd)
# lmp path
lmp_path = os.path.join(equi_path, 'meam')
os.makedirs(lmp_path, exist_ok=True)
print(lmp_path)
# lmp conf
conf_file = os.path.join(lmp_path, 'conf.lmp')
lammps.cvt_lammps_conf(to_poscar, os.path.relpath(conf_file))
ptypes = vasp.get_poscar_types(to_poscar)
lammps.apply_type_map(conf_file, type_map, ptypes)
# lmp input
fc = lammps.make_lammps_equi(os.path.basename(conf_file), ntypes,
lammps.inter_meam, meam_param)
with open(os.path.join(lmp_path, 'lammps.in'), 'w') as fp:
fp.write(fc)
# link models
os.chdir(lmp_path)
for ii in meam_potfile_name:
if os.path.exists(ii):
os.remove(ii)
for (ii, jj) in zip(meam_potfile, meam_potfile_name):
os.symlink(os.path.relpath(ii), jj)
os.chdir(cwd)
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]
conf_path = os.path.abspath(conf_dir)
equi_path = re.sub('confs', global_task_name, conf_path)
os.makedirs(equi_path, exist_ok=True)
cwd = os.getcwd()
from_poscar = os.path.join(conf_path, 'POSCAR')
to_poscar = os.path.join(equi_path, 'POSCAR')
if os.path.exists(to_poscar):
assert (filecmp.cmp(from_poscar, to_poscar))
else:
os.chdir(equi_path)
os.symlink(os.path.relpath(from_poscar), 'POSCAR')
os.chdir(cwd)
# lmp path
lmp_path = os.path.join(equi_path, 'deepmd')
os.makedirs(lmp_path, exist_ok=True)
print(lmp_path)
# lmp conf
conf_file = os.path.join(lmp_path, 'conf.lmp')
lammps.cvt_lammps_conf(to_poscar, os.path.relpath(conf_file))
ptypes = vasp.get_poscar_types(to_poscar)
lammps.apply_type_map(conf_file, deepmd_type_map, ptypes)
# lmp input
fc = lammps.make_lammps_equi(os.path.basename(conf_file), ntypes,
lammps.inter_deepmd, deepmd_models_name)
with open(os.path.join(lmp_path, 'lammps.in'), 'w') as fp:
fp.write(fc)
# link models
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)
os.chdir(cwd)
def make_lammps(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']
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]
assert len(model_name)>0,"No deepmd model in the model_dir"
else:
models = [os.path.join(model_dir,ii) for ii in model_name]
model_param = {'model_name' : fp_params['model_name'],
'param_type': fp_params['model_param_type']}
ntypes = len(type_map)
norm_def = jdata['norm_deform']
shear_def = jdata['shear_deform']
conf_path = os.path.abspath(conf_dir)
conf_poscar = os.path.join(conf_path, 'POSCAR')
# get equi poscar
equi_path = re.sub('confs', global_equi_name, conf_path)
equi_path = os.path.join(equi_path, task_type)
equi_dump = os.path.join(equi_path, 'dump.relax')
task_path = re.sub('confs', global_task_name, conf_path)
task_path = os.path.join(task_path, task_type)
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)
# get equi stress
equi_log = os.path.join(equi_path, 'log.lammps')
stress = lammps.get_stress(equi_log)
np.savetxt(os.path.join(task_path, 'equi.stress.out'), stress)
# gen strcture
# ss = Structure.from_file(conf_poscar)
# print(ss)
# ss = ss.from_file(task_poscar)
# print(ss)
ss = Structure.from_file(task_poscar)
# gen defomations
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]
print('gen with norm '+str(norm_strains))
print('gen with shear '+str(shear_strains))
dfm_ss = DeformedStructureSet(ss,
symmetry = False,
norm_strains = norm_strains,
shear_strains = shear_strains)
n_dfm = len(dfm_ss)
# gen tasks
cwd = os.getcwd()
# make lammps.in
if task_type=='deepmd':
fc = lammps.make_lammps_elastic('conf.lmp',
ntypes,
lammps.inter_deepmd,
model_name)
elif task_type=='meam':
fc = lammps.make_lammps_elastic('conf.lmp',
ntypes,
lammps.inter_meam,
model_param)
f_lammps_in = os.path.join(task_path, 'lammps.in')
with open(f_lammps_in, 'w') as fp :
fp.write(fc)
cwd = os.getcwd()
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]
for ii in range(n_dfm) :
# make dir
dfm_path = os.path.join(task_path, 'dfm-%03d' % ii)
os.makedirs(dfm_path, exist_ok=True)
os.chdir(dfm_path)
for jj in ['conf.lmp', 'lammps.in'] + model_name :
if os.path.isfile(jj):
os.remove(jj)
# make conf
dfm_ss.deformed_structures[ii].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)
# record strain
strain = Strain.from_deformation(dfm_ss.deformations[ii])
np.savetxt('strain.out', strain)
# 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)
cwd = os.getcwd()
def make_lammps(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']
if not model_name:
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': fp_params['model_name'],
'param_type': fp_params['model_param_type']
}
ntypes = len(type_map)
strain_start = jdata['strain_start']
strain_end = jdata['strain_end']
strain_step = jdata['strain_step']
strain_direct = jdata['strain_direct']
conf_path = os.path.abspath(conf_dir)
conf_poscar = os.path.join(conf_path, 'POSCAR')
# get equi poscar
equi_path = re.sub('confs', global_equi_name, conf_path)
equi_path = os.path.join(equi_path, task_type)
equi_dump = os.path.join(equi_path, 'dump.relax')
task_path = re.sub('confs', global_task_name, conf_path)
task_path = os.path.join(task_path, task_type)
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)
# get equi stress
equi_log = os.path.join(equi_path, 'log.lammps')
stress = lammps.get_stress(equi_log)
np.savetxt(os.path.join(task_path, 'equi.stress.out'), stress)
# gen strcture
ss = Structure.from_file(task_poscar)
# gen defomations
norm_strains = np.arange(strain_start, strain_end, strain_step)
print('gen with norm ' + str(norm_strains))
deformations = []
for ii in norm_strains:
strain = Strain.from_index_amount(strain_direct, ii)
deformations.append(strain.get_deformation_matrix())
deformed_structures = [
defo.apply_to_structure(ss) for defo in deformations
]
n_dfm = len(deformed_structures)
# gen tasks
cwd = os.getcwd()
# make lammps.in
if task_type == 'deepmd':
fc = lammps.make_lammps_elastic('conf.lmp', ntypes,
lammps.inter_deepmd, model_name)
elif task_type == 'meam':
fc = lammps.make_lammps_elastic('conf.lmp', ntypes, lammps.inter_meam,
model_param)
f_lammps_in = os.path.join(task_path, 'lammps.in')
with open(f_lammps_in, 'w') as fp:
fp.write(fc)
cwd = os.getcwd()
if task_type == 'deepmd':
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 = glob.glob(os.path.join(task_path, '*pb'))
else:
share_models = models
for ii in range(n_dfm):
# make dir
dfm_path = os.path.join(task_path, 'dfm-%03d' % ii)
os.makedirs(dfm_path, exist_ok=True)
os.chdir(dfm_path)
for jj in ['conf.lmp', 'lammps.in'] + model_name:
if os.path.isfile(jj):
os.remove(jj)
# make conf
deformed_structures[ii].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)
# record strain
strain = Strain.from_deformation(deformations[ii])
np.savetxt('strain.out', strain)
# 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)
cwd = os.getcwd()
def make_lammps(jdata,
conf_dir,
max_miller=2,
static=False,
relax_box=False,
task_type='wrong-task'):
kspacing = jdata['vasp_params']['kspacing']
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']
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]
assert len(model_name) > 0, "No deepmd model in the model_dir"
else:
models = [os.path.join(model_dir, ii) for ii in model_name]
model_param = {
'model_name': fp_params['model_name'],
'param_type': fp_params['model_param_type']
}
ntypes = len(type_map)
min_slab_size = jdata['min_slab_size']
min_vacuum_size = jdata['min_vacuum_size']
# get equi poscar
# conf_path = os.path.abspath(conf_dir)
# conf_poscar = os.path.join(conf_path, 'POSCAR')
equi_path = re.sub('confs', global_equi_name, conf_dir)
equi_path = os.path.join(equi_path, 'vasp-k%.2f' % kspacing)
equi_path = os.path.abspath(equi_path)
equi_contcar = os.path.join(equi_path, 'CONTCAR')
assert os.path.exists(
equi_contcar), "Please compute the equilibrium state using vasp first"
task_path = re.sub('confs', global_task_name, conf_dir)
task_path = os.path.abspath(task_path)
if static:
task_path = os.path.join(task_path, task_type + '-static')
else:
task_path = os.path.join(task_path, task_type)
os.makedirs(task_path, exist_ok=True)
cwd = os.getcwd()
os.chdir(task_path)
if os.path.isfile('POSCAR'):
os.remove('POSCAR')
os.symlink(os.path.relpath(equi_contcar), 'POSCAR')
os.chdir(cwd)
task_poscar = os.path.join(task_path, 'POSCAR')
# gen strcture
ss = Structure.from_file(task_poscar)
# gen slabs
all_slabs = generate_all_slabs(ss, max_miller, min_slab_size,
min_vacuum_size)
# make lammps.in
if task_type == 'deepmd':
if static:
fc = lammps.make_lammps_eval('conf.lmp', ntypes,
lammps.inter_deepmd, model_name)
else:
fc = lammps.make_lammps_equi('conf.lmp',
ntypes,
lammps.inter_deepmd,
model_name,
change_box=relax_box)
elif task_type == 'meam':
if static:
fc = lammps.make_lammps_eval('conf.lmp', ntypes, lammps.inter_meam,
model_param)
else:
fc = lammps.make_lammps_equi('conf.lmp',
ntypes,
lammps.inter_meam,
model_param,
change_box=relax_box)
f_lammps_in = os.path.join(task_path, 'lammps.in')
with open(f_lammps_in, 'w') as fp:
fp.write(fc)
cwd = os.getcwd()
if task_type == 'deepmd':
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 = glob.glob(os.path.join(task_path, '*pb'))
else:
share_models = models
for ii in range(len(all_slabs)):
slab = all_slabs[ii]
miller_str = "m%d.%d.%dm" % (
slab.miller_index[0], slab.miller_index[1], slab.miller_index[2])
# make dir
struct_path = os.path.join(task_path,
'struct-%03d-%s' % (ii, miller_str))
os.makedirs(struct_path, exist_ok=True)
os.chdir(struct_path)
for jj in ['conf.lmp', 'lammps.in'] + model_name:
if os.path.isfile(jj):
os.remove(jj)
print("# %03d generate " % ii, struct_path,
" \t %d atoms" % len(slab.sites))
# make conf
slab.to('POSCAR', 'POSCAR')
vasp.regulate_poscar('POSCAR', 'POSCAR')
lammps.cvt_lammps_conf('POSCAR', 'conf.lmp')
ptypes = vasp.get_poscar_types('POSCAR')
lammps.apply_type_map('conf.lmp', type_map, ptypes)
# record miller
np.savetxt('miller.out', slab.miller_index, fmt='%d')
# 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)
cwd = os.getcwd()
def _make_meam_lammps(jdata, conf_dir, supercell, insert_ele, task_name):
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']
}
conf_path = os.path.abspath(conf_dir)
conf_poscar = os.path.join(conf_path, 'POSCAR')
# get equi poscar
equi_path = re.sub('confs', global_equi_name, conf_path)
equi_path = os.path.join(equi_path, task_name)
equi_dump = os.path.join(equi_path, 'dump.relax')
task_path = re.sub('confs', global_task_name, conf_path)
task_path = os.path.join(task_path, task_name)
os.makedirs(task_path, exist_ok=True)
task_poscar = os.path.join(task_path, 'POSCAR')
cwd = os.getcwd()
os.chdir(task_path)
lammps.poscar_from_last_dump(equi_dump, task_poscar, type_map)
os.chdir(cwd)
# gen structure from equi poscar
ss = Structure.from_file(task_poscar)
# gen defects
vds = InterstitialGenerator(ss, insert_ele)
dss = []
for jj in vds:
dss.append(jj.generate_defect_structure(supercell))
# gen tasks
cwd = os.getcwd()
# make lammps.in, relax at 0 bar (scale = 1)
fc = lammps.make_lammps_press_relax('conf.lmp', ntypes, 1,
lammps.inter_meam, meam_param)
f_lammps_in = os.path.join(task_path, 'lammps.in')
with open(f_lammps_in, 'w') as fp:
fp.write(fc)
# gen tasks
copy_str = "%sx%sx%s" % (supercell[0], supercell[1], supercell[2])
cwd = os.getcwd()
for ii in range(len(dss)):
struct_path = os.path.join(
task_path, 'struct-%s-%s-%03d' % (insert_ele, copy_str, ii))
print('# generate %s' % (struct_path))
os.makedirs(struct_path, exist_ok=True)
os.chdir(struct_path)
for jj in ['conf.lmp', 'lammps.in'] + meam_potfile_name:
if os.path.isfile(jj):
os.remove(jj)
# make conf
dss[ii].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)
# save supercell
np.savetxt('supercell.out', supercell, fmt='%d')
os.chdir(cwd)
def make_input_file(self, output_dir, task_type, task_param):
lammps.cvt_lammps_conf(os.path.join(output_dir, 'POSCAR'), 'conf.lmp')
with open(os.path.join(output_dir, 'task.json'), 'w') as fp:
json.dump(task_param, fp, indent=4)
# lines in lammps.in related to model
# line_model = "pair_style meam \n"
# line_model += "pair_coeff * * %s " % (os.path.basename(self.model[0]))
# for ii in self.type_map:
# line_model += ii + ' '
# line_model += "%s " % (os.path.basename(self.model[1]))
# for ii in self.type_map:
# line_model += ii + ' '
# line_model += '\n'
etol = 1e-12
ftol = 1e-6
maxiter = 5000
maxeval = 500000
change_box = True
B0 = 70
bp = 0
scale2equi = 1
ntypes = len(self.type_map)
reprod_opt = False
static = False
if 'etol' in task_param:
etol = task_param['etol']
if 'ftol' in task_param:
ftol = task_param['ftol']
if 'maxiter' in task_param:
maxiter = task_param['maxiter']
if 'maxeval' in task_param:
maxeval = task_param['maxeval']
if 'change_box' in task_param:
change_box = task_param['change_box']
if 'scale2equi' in task_param:
scale2equi = task_param['scale2equi']
if 'reprod_opt' in task_param:
reprod_opt = task_param['reprod_opt']
if 'static-opt' in task_param:
static = task_param['static-opt']
model_name = list(map(os.path.basename, self.model))
model_param = {'model_name': model_name, 'param_type': self.type_map}
fc = ''
if task_type == 'relaxation' \
or (task_type == 'eos' and not change_box) \
or (task_type == 'surface' and not static):
fc = lammps.make_lammps_equi('conf.lmp', ntypes, lammps.inter_meam,
model_param, etol, ftol, maxiter,
maxeval, change_box)
if task_type == 'static' \
or (task_type == 'surface' and static):
fc = lammps.make_lammps_eval('conf.lmp', ntypes, lammps.inter_meam,
model_param)
if task_type == 'elastic':
fc = lammps.make_lammps_elastic('conf.lmp', ntypes,
lammps.inter_meam, model_param,
etol, ftol, maxiter, maxeval)
if task_type == 'vacancy' \
or (task_type == 'eos' and change_box) \
or (task_type == 'interstitial'):
fc = lammps.make_lammps_press_relax('conf.lmp', ntypes, scale2equi,
lammps.inter_meam, model_param,
B0, bp, etol, ftol, maxiter,
maxeval)
if reprod_opt:
fc = lammps.make_lammps_eval('conf.lmp', ntypes, lammps.inter_meam,
model_param)
with open(os.path.join(output_dir, 'in.lammps'), 'w') as fp:
fp.write(fc)
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]
supercell_matrix = jdata['supercell_matrix']
band_path = jdata['band']
conf_path = os.path.abspath(conf_dir)
conf_poscar = os.path.join(conf_path, 'POSCAR')
# get equi poscar
equi_path = re.sub('confs', global_equi_name, conf_path)
equi_path = os.path.join(equi_path, 'deepmd')
task_path = re.sub('confs', global_task_name, conf_path)
task_path = os.path.join(task_path, 'deepmd')
os.makedirs(task_path, exist_ok=True)
task_poscar = os.path.join(task_path, 'POSCAR')
cwd = os.getcwd()
os.chdir(task_path)
if os.path.isfile('POSCAR'):
os.remove('POSCAR')
os.symlink(os.path.relpath(conf_poscar), 'POSCAR')
os.chdir(cwd)
with open(task_poscar, 'r') as fp:
lines = fp.read().split('\n')
ele_list = lines[5].split()
print(task_path)
# make conf.lmp
conf_file = os.path.join(task_path, 'conf.lmp')
lammps.cvt_lammps_conf(task_poscar, os.path.relpath(conf_file))
ptypes = vasp.get_poscar_types(task_poscar)
lammps.apply_type_map(conf_file, deepmd_type_map, ptypes)
# make lammps.in
ntypes = len(ele_list)
unitcell = PhonopyAtoms(symbols=ele_list,
cell=(np.eye(3)),
scaled_positions=np.zeros((ntypes, 3)))
fc = lammps.make_lammps_phonon('conf.lmp', unitcell.masses,
lammps.inter_deepmd, deepmd_models_name)
f_lammps_in = os.path.join(task_path, 'lammps.in')
with open(f_lammps_in, 'w') as fp:
fp.write(fc)
cwd = os.getcwd()
# link models
os.chdir(task_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)
# gen band.conf
os.chdir(task_path)
with open('band.conf', 'w') as fp:
fp.write('ATOM_NAME = ')
for ii in ele_list:
fp.write(ii)
fp.write(' ')
fp.write('\n')
fp.write(
'DIM = %d %d %d\n' %
(supercell_matrix[0], supercell_matrix[1], supercell_matrix[2]))
fp.write('BAND = %s\n' % band_path)
fp.write('FORCE_CONSTANTS=READ')
# gen task
'''
phlammps = Phonolammps('lammps.in',supercell_matrix=supercell_matrix)
unitcell = phlammps.get_unitcell()
phonon = Phonopy(unitcell,supercell_matrix)
phonon.save(filename='phonopy_disp.yaml')
with open('phonopy_disp.yaml', 'r') as f:
temp = yaml.load(f.read())
with open('POSCAR-unitcell', 'w') as fp:
for ii in ele_list:
fp.write(ii)
fp.write(' ')
fp.write('\n')
data=open('POSCAR', 'r')
next(data)
fp.write(data.readline())
for ii in temp['unit_cell']['lattice']:
fp.write(str(ii).replace(',', '').replace('[', '').replace(']','\n'))
for ii in ele_list:
fp.write(str(str(temp['unit_cell']['points']).count(ii)))
fp.write(' ')
fp.write('\n')
fp.write('Direct\n')
for ii in temp['unit_cell']['points']:
fp.write(str(ii['coordinates']).replace(',', '').replace('[', '').replace(']', '\n'))
'''
os.system('phonolammps lammps.in --dim %d %d %d -c POSCAR-unitcell' %
(supercell_matrix[0], supercell_matrix[1], supercell_matrix[2]))
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_meam_lammps(jdata,
conf_dir,
max_miller=2,
static=False,
relax_box=False,
task_name='wrong-task'):
fp_params = jdata['vasp_params']
kspacing = fp_params['kspacing']
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']
}
min_slab_size = jdata['min_slab_size']
min_vacuum_size = jdata['min_vacuum_size']
# get equi poscar
# conf_path = os.path.abspath(conf_dir)
# conf_poscar = os.path.join(conf_path, 'POSCAR')
equi_path = re.sub('confs', global_equi_name, conf_dir)
equi_path = os.path.join(equi_path, 'vasp-k%.2f' % kspacing)
equi_path = os.path.abspath(equi_path)
equi_contcar = os.path.join(equi_path, 'CONTCAR')
task_path = re.sub('confs', global_task_name, conf_dir)
task_path = os.path.abspath(task_path)
task_path = os.path.join(task_path, task_name)
os.makedirs(task_path, exist_ok=True)
cwd = os.getcwd()
os.chdir(task_path)
if os.path.isfile('POSCAR'):
os.remove('POSCAR')
os.symlink(os.path.relpath(equi_contcar), 'POSCAR')
os.chdir(cwd)
task_poscar = os.path.join(task_path, 'POSCAR')
# gen strcture
ss = Structure.from_file(task_poscar)
# gen slabs
all_slabs = generate_all_slabs(ss, max_miller, min_slab_size,
min_vacuum_size)
# make lammps.in
if static:
fc = lammps.make_lammps_eval('conf.lmp', ntypes, lammps.inter_meam,
meam_param)
else:
fc = lammps.make_lammps_equi('conf.lmp',
ntypes,
lammps.inter_meam,
meam_param,
change_box=relax_box)
f_lammps_in = os.path.join(task_path, 'lammps.in')
with open(f_lammps_in, 'w') as fp:
fp.write(fc)
cwd = os.getcwd()
for ii in range(len(all_slabs)):
slab = all_slabs[ii]
miller_str = "m%d.%d.%dm" % (
slab.miller_index[0], slab.miller_index[1], slab.miller_index[2])
# make dir
struct_path = os.path.join(task_path,
'struct-%03d-%s' % (ii, miller_str))
os.makedirs(struct_path, exist_ok=True)
os.chdir(struct_path)
for jj in ['conf.lmp', 'lammps.in'] + meam_potfile_name:
if os.path.isfile(jj):
os.remove(jj)
print("# %03d generate " % ii, struct_path,
" \t %d atoms" % len(slab.sites))
# make conf
slab.to('POSCAR', 'POSCAR')
vasp.regulate_poscar('POSCAR', 'POSCAR')
lammps.cvt_lammps_conf('POSCAR', 'conf.lmp')
ptypes = vasp.get_poscar_types('POSCAR')
lammps.apply_type_map('conf.lmp', type_map, ptypes)
# record miller
np.savetxt('miller.out', slab.miller_index, fmt='%d')
# 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)
cwd = os.getcwd()
def make_input_file(self,
output_dir,
task_type,
task_param):
lammps.cvt_lammps_conf(os.path.join(output_dir, 'POSCAR'), os.path.join(output_dir, 'conf.lmp'), lammps.element_list(self.type_map))
# dumpfn(task_param, os.path.join(output_dir, 'task.json'), indent=4)
etol = 1e-12
ftol = 1e-6
maxiter = 5000
maxeval = 500000
B0 = 70
bp = 0
ntypes = len(self.type_map)
cal_type = task_param['cal_type']
cal_setting = task_param['cal_setting']
self.set_model_param()
# deal with user input in.lammps for relaxation
if os.path.isfile(self.in_lammps) and task_type == 'relaxation':
with open(self.in_lammps, 'r') as fin:
fc = fin.read()
# user input in.lammps for property calculation
if 'input_prop' in cal_setting and os.path.isfile(cal_setting['input_prop']):
with open(os.path.abspath(cal_setting['input_prop']), 'r') as fin:
fc = fin.read()
else:
if 'etol' in cal_setting:
dlog.info("%s setting etol to %s" % (self.make_input_file.__name__, cal_setting['etol']))
etol = cal_setting['etol']
if 'ftol' in cal_setting:
dlog.info("%s setting ftol to %s" % (self.make_input_file.__name__, cal_setting['ftol']))
ftol = cal_setting['ftol']
if 'maxiter' in cal_setting:
dlog.info("%s setting maxiter to %s" % (self.make_input_file.__name__, cal_setting['maxiter']))
maxiter = cal_setting['maxiter']
if 'maxeval' in cal_setting:
dlog.info("%s setting maxeval to %s" % (self.make_input_file.__name__, cal_setting['maxeval']))
maxeval = cal_setting['maxeval']
if cal_type == 'relaxation':
relax_pos = cal_setting['relax_pos']
relax_shape = cal_setting['relax_shape']
relax_vol = cal_setting['relax_vol']
if [relax_pos, relax_shape, relax_vol] == [True, False, False]:
fc = lammps.make_lammps_equi('conf.lmp', self.type_map, self.inter_func, self.model_param,
etol, ftol, maxiter, maxeval, False)
elif [relax_pos, relax_shape, relax_vol] == [True, True, True]:
fc = lammps.make_lammps_equi('conf.lmp', self.type_map, self.inter_func, self.model_param,
etol, ftol, maxiter, maxeval, True)
elif [relax_pos, relax_shape, relax_vol] == [True, True, False] and not task_type == 'eos':
if 'scale2equi' in task_param:
scale2equi = task_param['scale2equi']
fc = lammps.make_lammps_press_relax('conf.lmp', self.type_map, scale2equi[int(output_dir[-6:])],
self.inter_func,
self.model_param, B0, bp, etol, ftol, maxiter, maxeval)
else:
fc = lammps.make_lammps_equi('conf.lmp', self.type_map, self.inter_func, self.model_param,
etol, ftol, maxiter, maxeval, True)
elif [relax_pos, relax_shape, relax_vol] == [True, True, False] and task_type == 'eos':
task_param['cal_setting']['relax_shape'] = False
fc = lammps.make_lammps_equi('conf.lmp', self.type_map, self.inter_func, self.model_param,
etol, ftol, maxiter, maxeval, False)
elif [relax_pos, relax_shape, relax_vol] == [False, False, False]:
fc = lammps.make_lammps_eval('conf.lmp', self.type_map, self.inter_func, self.model_param)
else:
raise RuntimeError("not supported calculation setting for LAMMPS")
elif cal_type == 'static':
fc = lammps.make_lammps_eval('conf.lmp', self.type_map, self.inter_func, self.model_param)
else:
raise RuntimeError("not supported calculation type for LAMMPS")
dumpfn(task_param, os.path.join(output_dir, 'task.json'), indent=4)
in_lammps_not_link_list = ['eos']
if task_type not in in_lammps_not_link_list:
with open(os.path.join(output_dir, '../in.lammps'), 'w') as fp:
fp.write(fc)
cwd = os.getcwd()
os.chdir(output_dir)
if not (os.path.islink('in.lammps') or os.path.isfile('in.lammps')):
os.symlink('../in.lammps', 'in.lammps')
else:
os.remove('in.lammps')
os.symlink('../in.lammps', 'in.lammps')
os.chdir(cwd)
else:
with open(os.path.join(output_dir, 'in.lammps'), 'w') as fp:
fp.write(fc)
def make_lammps(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']
if not model_name:
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': fp_params['model_name'],
'param_type': fp_params['model_param_type']
}
supercell_matrix = jdata['supercell_matrix']
band_path = jdata['band']
conf_path = os.path.abspath(conf_dir)
conf_poscar = os.path.join(conf_path, 'POSCAR')
# get equi poscar
equi_path = re.sub('confs', global_equi_name, conf_path)
equi_path = os.path.join(equi_path, task_type)
task_path = re.sub('confs', global_task_name, conf_path)
task_path = os.path.join(task_path, task_type)
os.makedirs(task_path, exist_ok=True)
task_poscar = os.path.join(task_path, 'POSCAR')
cwd = os.getcwd()
os.chdir(task_path)
if os.path.isfile('POSCAR'):
os.remove('POSCAR')
os.symlink(os.path.relpath(conf_poscar), 'POSCAR')
os.chdir(cwd)
with open(task_poscar, 'r') as fp:
lines = fp.read().split('\n')
ele_list = lines[5].split()
print(task_path)
# make conf.lmp
conf_file = os.path.join(task_path, 'conf.lmp')
lammps.cvt_lammps_conf(task_poscar, os.path.relpath(conf_file))
ptypes = vasp.get_poscar_types(task_poscar)
lammps.apply_type_map(conf_file, type_map, ptypes)
# make lammps.in
ntypes = len(ele_list)
unitcell = get_structure_from_poscar(task_poscar)
if task_type == 'deepmd':
fc = lammps.make_lammps_phonon('conf.lmp', unitcell.masses,
lammps.inter_deepmd, model_name)
if task_type == 'meam':
fc = lammps.make_lammps_phonon('conf.lmp', unitcell.masses,
lammps.inter_meam, model_param)
f_lammps_in = os.path.join(task_path, 'lammps.in')
with open(f_lammps_in, 'w') as fp:
fp.write(fc)
cwd = os.getcwd()
# link models
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)
# gen band.conf
os.chdir(task_path)
with open('band.conf', 'w') as fp:
fp.write('ATOM_NAME = ')
for ii in ele_list:
fp.write(ii)
fp.write(' ')
fp.write('\n')
fp.write(
'DIM = %d %d %d\n' %
(supercell_matrix[0], supercell_matrix[1], supercell_matrix[2]))
fp.write('BAND = %s\n' % band_path)
fp.write('FORCE_CONSTANTS=READ\n')
os.system('phonolammps lammps.in --dim %d %d %d -c POSCAR' %
(supercell_matrix[0], supercell_matrix[1], supercell_matrix[2]))
def _make_reprod_traj(jdata, conf_dir, supercell, insert_ele, task_type):
kspacing = jdata['vasp_params']['kspacing']
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']
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]
assert len(model_name) > 0, "No deepmd model in the model_dir"
else:
models = [os.path.join(model_dir, ii) for ii in model_name]
model_param = {
'model_name': fp_params['model_name'],
'param_type': fp_params['model_param_type']
}
ntypes = len(type_map)
conf_path = os.path.abspath(conf_dir)
task_path = re.sub('confs', global_task_name, conf_path)
vasp_path = os.path.join(task_path, 'vasp-k%.2f' % kspacing)
lmps_path = os.path.join(task_path, task_type + '-reprod-k%.2f' % kspacing)
os.makedirs(lmps_path, exist_ok=True)
copy_str = "%sx%sx%s" % (supercell[0], supercell[1], supercell[2])
struct_widecard = os.path.join(vasp_path,
'struct-%s-%s-*' % (insert_ele, copy_str))
vasp_struct = glob.glob(struct_widecard)
vasp_struct.sort()
cwd = os.getcwd()
# make lammps.in
if task_type == 'deepmd':
fc = lammps.make_lammps_eval('conf.lmp', ntypes, lammps.inter_deepmd,
model_name)
elif task_type == 'meam':
fc = lammps.make_lammps_eval('conf.lmp', ntypes, lammps.inter_meam,
model_param)
f_lammps_in = os.path.join(lmps_path, 'lammps.in')
with open(f_lammps_in, 'w') as fp:
fp.write(fc)
for vs in vasp_struct:
# get vasp energy
outcar = os.path.join(vs, 'OUTCAR')
energies = vasp.get_energies(outcar)
# get xdat
xdatcar = os.path.join(vs, 'XDATCAR')
struct_basename = os.path.basename(vs)
ls = os.path.join(lmps_path, struct_basename)
print(ls)
os.makedirs(ls, exist_ok=True)
os.chdir(ls)
if os.path.exists('XDATCAR'):
os.remove('XDATCAR')
os.symlink(os.path.relpath(xdatcar), 'XDATCAR')
xdat_lines = open('XDATCAR', 'r').read().split('\n')
natoms = vasp.poscar_natoms('XDATCAR')
xdat_secsize = natoms + 8
xdat_nframes = len(xdat_lines) // xdat_secsize
if xdat_nframes > len(energies):
warnings.warn(
'nframes %d in xdat is larger than energy %d, use the last %d frames'
% (xdat_nframes, len(energies), len(energies)))
xdat_nlines = len(energies) * xdat_secsize
xdat_lines = xdat_lines[xdat_nlines:]
xdat_nframes = len(xdat_lines) // xdat_secsize
print(xdat_nframes, len(energies))
#link lammps.in and model
for jj in ['lammps.in'] + model_name:
if os.path.islink(jj):
os.unlink(jj)
os.symlink(os.path.relpath(f_lammps_in), 'lammps.in')
if task_type == 'deepmd':
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 = glob.glob(os.path.join(ls, '*pb'))
else:
share_models = models
# loop over frames
for ii in range(xdat_nframes):
frame_path = 'frame.%06d' % ii
os.makedirs(frame_path, exist_ok=True)
os.chdir(frame_path)
# clear dir
for jj in ['conf.lmp']:
if os.path.isfile(jj):
os.remove(jj)
for jj in ['lammps.in'] + model_name:
if os.path.islink(jj):
os.unlink(jj)
# link lammps in
os.symlink(os.path.relpath('../lammps.in'), 'lammps.in')
# make conf
with open('POSCAR', 'w') as fp:
fp.write('\n'.join(xdat_lines[ii * xdat_secsize:(ii + 1) *
xdat_secsize]))
lammps.cvt_lammps_conf('POSCAR', 'conf.lmp')
ptypes = vasp.get_poscar_types('POSCAR')
lammps.apply_type_map('conf.lmp', type_map, ptypes)
# link models
for (kk, ll) in zip(share_models, model_name):
os.symlink(os.path.relpath(kk), ll)
os.chdir(ls)
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)
def _make_lammps(jdata, conf_dir, supercell, insert_ele, task_type):
fp_params = jdata['vasp_params']
kspacing = fp_params['kspacing']
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']
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]
assert len(model_name) > 0, "No deepmd model in the model_dir"
else:
models = [os.path.join(model_dir, ii) for ii in model_name]
model_param = {
'model_name': fp_params['model_name'],
'param_type': fp_params['model_param_type']
}
ntypes = len(type_map)
conf_path = os.path.abspath(conf_dir)
conf_poscar = os.path.join(conf_path, 'POSCAR')
# get equi poscar
equi_path = re.sub('confs', global_equi_name, conf_path)
equi_path = os.path.join(equi_path, 'vasp-k%.2f' % kspacing)
equi_contcar = os.path.join(equi_path, 'CONTCAR')
#equi_path = os.path.join(equi_path, task_type)
#equi_dump = os.path.join(equi_path, 'dump.relax')
assert os.path.exists(
equi_contcar), "Please compute the equilibrium state using vasp first"
task_path = re.sub('confs', global_task_name, conf_path)
task_path = os.path.join(task_path, task_type)
os.makedirs(task_path, exist_ok=True)
task_poscar = os.path.join(task_path, 'POSCAR')
cwd = os.getcwd()
os.chdir(task_path)
if os.path.isfile('POSCAR'):
os.remove('POSCAR')
os.symlink(os.path.relpath(equi_contcar), 'POSCAR')
#lammps.poscar_from_last_dump(equi_dump, task_poscar, type_map)
os.chdir(cwd)
# gen structure from equi poscar
print("task poscar: ", task_poscar)
ss = Structure.from_file(task_poscar)
# gen defects
vds = InterstitialGenerator(ss, insert_ele)
dss = []
for jj in vds:
dss.append(jj.generate_defect_structure(supercell))
# gen tasks
cwd = os.getcwd()
# make lammps.in, relax at 0 bar (scale = 1)
if task_type == 'deepmd':
fc = lammps.make_lammps_press_relax('conf.lmp', ntypes, 1,
lammps.inter_deepmd, model_name)
elif task_type == 'meam':
fc = lammps.make_lammps_press_relax('conf.lmp', ntypes, 1,
lammps.inter_meam, model_param)
f_lammps_in = os.path.join(task_path, 'lammps.in')
with open(f_lammps_in, 'w') as fp:
fp.write(fc)
# gen tasks
copy_str = "%sx%sx%s" % (supercell[0], supercell[1], supercell[2])
cwd = os.getcwd()
if task_type == 'deepmd':
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 = glob.glob(os.path.join(task_path, '*pb'))
else:
share_models = models
for ii in range(len(dss)):
struct_path = os.path.join(
task_path, 'struct-%s-%s-%03d' % (insert_ele, copy_str, ii))
print('# generate %s' % (struct_path))
os.makedirs(struct_path, exist_ok=True)
os.chdir(struct_path)
for jj in ['conf.lmp', 'lammps.in'] + model_name:
if os.path.isfile(jj):
os.remove(jj)
# make conf
dss[ii].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)
# save supercell
np.savetxt('supercell.out', supercell, fmt='%d')
os.chdir(cwd)
def make_lammps(jdata, conf_dir, supercell,task_type) :
kspacing = jdata['vasp_params']['kspacing']
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']
if not model_name :
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' : fp_params['model_name'],
'param_type': fp_params['model_param_type']}
ntypes = len(type_map)
conf_path = os.path.abspath(conf_dir)
conf_poscar = os.path.join(conf_path, 'POSCAR')
# get equi poscar
equi_path = re.sub('confs', global_equi_name, conf_path)
equi_path = os.path.join(equi_path, 'vasp-k%.2f' % kspacing)
equi_contcar = os.path.join(equi_path, 'CONTCAR')
# equi_path = re.sub('confs', global_equi_name, conf_path)
# equi_path = os.path.join(equi_path, 'lmp')
# equi_dump = os.path.join(equi_path, 'dump.relax')
task_path = re.sub('confs', global_task_name, conf_path)
task_path = os.path.join(task_path, task_type)
os.makedirs(task_path, exist_ok=True)
# gen task poscar
task_poscar = os.path.join(task_path, 'POSCAR')
# lammps.poscar_from_last_dump(equi_dump, task_poscar, deepmd_type_map)
cwd = os.getcwd()
os.chdir(task_path)
if os.path.isfile('POSCAR') :
os.remove('POSCAR')
os.symlink(os.path.relpath(equi_contcar), 'POSCAR')
os.chdir(cwd)
# gen structure from equi poscar
edge = Structure.from_file(task_poscar)
edge.make_supercell([supercell[0],supercell[1],1])
center=int(supercell[0]*int(supercell[1]/2)+supercell[0]/2)
s=[center+supercell[0]*ii for ii in range(int(supercell[1]/2+1))]
# gen edge dislocation
edge.remove_sites(s)
edge.make_supercell([1,1,supercell[2]])
# gen screw dislocation
screw = Structure.from_file(task_poscar)
screw.make_supercell([supercell[0], supercell[1], supercell[2]],to_unit_cell=False)
c=[]
for jj in range(math.ceil(supercell[0]/2)):
for ii in range(supercell[2]):
c.append(ii+jj*supercell[2])
v0 = np.asarray(screw._sites[0].coords, float) - np.asarray(screw._sites[1].coords, float)
for kk in range(math.ceil(supercell[1]/2)):
dc=[ii+kk*supercell[0]*supercell[2] for ii in c]
v=(math.ceil(supercell[1]/2)-kk)/math.ceil(supercell[1]/2)*v0
screw.translate_sites(dc, vector=v, frac_coords=False, to_unit_cell=False)
dss = []
dss.append(edge)
dss.append(screw)
# gen tasks
cwd = os.getcwd()
# make lammps.in, relax at 0 bar (scale = 1)
if task_type=='deepmd':
fc = lammps.make_lammps_elastic('conf.lmp',
ntypes,
lammps.inter_deepmd,
model_name)
elif task_type =='meam':
fc = lammps.make_lammps_elastic('conf.lmp',
ntypes,
lammps.inter_meam,
model_param)
f_lammps_in = os.path.join(task_path, 'lammps.in')
with open(f_lammps_in, 'w') as fp :
fp.write(fc)
# gen tasks
copy_str = "%sx%sx%s" % (supercell[0], supercell[1], supercell[2])
cwd = os.getcwd()
if task_type=='deepmd':
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 = glob.glob(os.path.join(task_path, '*pb'))
else :
share_models=models
for ii in range(len(dss)) :
struct_path = os.path.join(task_path, 'struct-%s-%s' % (copy_str,task_dict[ii]))
print('# generate %s' % (struct_path))
os.makedirs(struct_path, exist_ok=True)
os.chdir(struct_path)
for jj in ['conf.lmp', 'lammps.in'] + model_name :
if os.path.isfile(jj):
os.remove(jj)
# make conf
dss[ii].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)
# save supercell
np.savetxt('supercell.out', supercell, fmt='%d')
os.chdir(cwd)
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_deepmd_lammps(jdata, conf_dir, supercell) :
fp_params = jdata['vasp_params']
kspacing = fp_params['kspacing']
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]
conf_path = os.path.abspath(conf_dir)
conf_poscar = os.path.join(conf_path, 'POSCAR')
# get equi poscar
equi_path = re.sub('confs', global_equi_name, conf_path)
equi_path = os.path.join(equi_path, 'vasp-k%.2f' % kspacing)
equi_contcar = os.path.join(equi_path, 'CONTCAR')
# equi_path = re.sub('confs', global_equi_name, conf_path)
# equi_path = os.path.join(equi_path, 'lmp')
# equi_dump = os.path.join(equi_path, 'dump.relax')
task_path = re.sub('confs', global_task_name, conf_path)
task_path = os.path.join(task_path, 'deepmd')
os.makedirs(task_path, exist_ok=True)
# gen task poscar
task_poscar = os.path.join(task_path, 'POSCAR')
# lammps.poscar_from_last_dump(equi_dump, task_poscar, deepmd_type_map)
cwd = os.getcwd()
os.chdir(task_path)
if os.path.isfile('POSCAR') :
os.remove('POSCAR')
os.symlink(os.path.relpath(equi_contcar), 'POSCAR')
os.chdir(cwd)
# gen structure from equi poscar
ss = Structure.from_file(task_poscar)
# gen defects
vds = VacancyGenerator(ss)
dss = []
for jj in vds :
dss.append(jj.generate_defect_structure(supercell))
# gen tasks
cwd = os.getcwd()
# make lammps.in, relax at 0 bar (scale = 1)
fc = lammps.make_lammps_press_relax('conf.lmp',
ntypes,
1,
lammps.inter_deepmd,
deepmd_models_name)
f_lammps_in = os.path.join(task_path, 'lammps.in')
with open(f_lammps_in, 'w') as fp :
fp.write(fc)
# gen tasks
copy_str = "%sx%sx%s" % (supercell[0], supercell[1], supercell[2])
cwd = os.getcwd()
os.chdir(task_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(task_path, '*pb'))
for ii in range(len(dss)) :
struct_path = os.path.join(task_path, 'struct-%s-%03d' % (copy_str,ii))
print('# generate %s' % (struct_path))
os.makedirs(struct_path, exist_ok=True)
os.chdir(struct_path)
for jj in ['conf.lmp', 'lammps.in'] + deepmd_models_name :
if os.path.isfile(jj):
os.remove(jj)
# make conf
dss[ii].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 lammps.in
os.symlink(os.path.relpath(f_lammps_in), 'lammps.in')
# link models
for (ii,jj) in zip(share_models, deepmd_models_name) :
os.symlink(os.path.relpath(ii), jj)
# save supercell
np.savetxt('supercell.out', supercell, fmt='%d')
os.chdir(cwd)
