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)