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_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_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, 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)
