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_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_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_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_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)