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_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()
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(deepmd_models, deepmd_models_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)
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(deepmd_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)