def vac_antisite_def_struct_gen(c_size=15, mpid="", struct=None, write_file=True): """ Vacancy, antisite generator Args: c_size: cell size struct: Structure object or mpid: materials project id Returns: def_str: defect structures in Poscar object format """ def_str = [] if struct == None: with MPRester() as mp: struct = mp.get_structure_by_material_id(mpid) if mpid == "": print("Provide structure") c_size = c_size prim_struct_sites = len(struct.sites) struct = SpacegroupAnalyzer(struct).get_conventional_standard_structure() dim1 = int((float(c_size) / float(max(abs(struct.lattice.matrix[0]))))) + 1 dim2 = int(float(c_size) / float(max(abs(struct.lattice.matrix[1])))) + 1 dim3 = int(float(c_size) / float(max(abs(struct.lattice.matrix[2])))) + 1 cellmax = max(dim1, dim2, dim3) conv_struct_sites = len(struct.sites) conv_prim_rat = int(conv_struct_sites / prim_struct_sites) sc_scale = [dim1, dim2, dim3] print("sc_scale", sc_scale) tmp = struct.copy() tmp.make_supercell(sc_scale) sc_tmp = tmp # Poscar(tmp).structure .make_supercell(list(sc_scale)) scs = list(VacancyGenerator(struct)) supercell = Poscar(sc_tmp) supercell.comment = str("bulk") + str("@") + str("cellmax") + str(cellmax) def_str.append(supercell) if write_file == True: supercell.write_file("POSCAR-" + str("bulk") + str(".vasp")) for i in range(len(scs)): sc = scs[i].generate_defect_structure(sc_scale) poscar = Poscar(sc) # mpvis.get_poscar(sc) pmg_name = str(scs[i].name).split("_") sitespecie = pmg_name[1] mult = pmg_name[2].split("mult")[1] name = (str("vacancy_") + str(i + 1) + str("_mult-") + str(mult) + str("_sitespecie-") + str(sitespecie) + str("@cellmax") + str(cellmax)) poscar.comment = str(name) def_str.append(poscar) if write_file == True: filename = (str("POSCAR-") + str("vacancy_") + str(i + 1) + str("_mult-") + str(mult) + str("_sitespecie-") + str(sitespecie) + str(".vasp")) poscar.write_file(filename) return def_str
def test_charge_gen(self): struc = PymatgenTest.get_structure("VO2") # assemble set of defects to get charges for vac_gen = VacancyGenerator(struc) vacs = list(vac_gen) full_subs = [] for sub_elt in ["V", "O", "S"]: sub_gen = SubstitutionGenerator(struc, sub_elt) full_subs.extend(list(sub_gen)) int_gen = VoronoiInterstitialGenerator(struc, "H") inters = list(int_gen) defect_list = list(set().union(vacs, full_subs, inters)) # test simple charges true_charges = { "Vac_O_mult4": 2, "Int_H_Voronoi1_mult8": 0, "Int_H_Voronoi2_mult8": 0, "Vac_V_mult2": -4, "Sub_S_on_V_mult2": 0, "Int_H_Voronoi3_mult4": 0, "Int_H_Voronoi4_mult4": 0, "Sub_O_on_V_mult2": -2, "Sub_S_on_O_mult4": 0, "Sub_V_on_O_mult4": 1, } for defect in defect_list: scg = SimpleChargeGenerator(defect) charged_defects_list = list(scg) def_name = charged_defects_list[0].name charge = charged_defects_list[0].charge self.assertEqual(len(charged_defects_list), 1) self.assertEqual(true_charges[def_name], charge)
def test_vacancy_gen_charges(self): # Ensure correct BV charges are assigned struc = PymatgenTest.get_structure("VO2") vac_gen = VacancyGenerator(struc, include_bv_charge=True) for vac in vac_gen: if str(vac.site.specie) == "V": self.assertEqual(vac.charge, -4) if str(vac.site.specie) == "O": self.assertEqual(vac.charge, 2)
def test_vacancy_gen(self): struc = PymatgenTest.get_structure("VO2") vac_gen = VacancyGenerator(struc) vacs = list(vac_gen) self.assertEqual(len(vacs), 2) multiplicities = {str(v.site.specie): v.multiplicity for v in vacs} self.assertEqual(multiplicities, {"O": 4, "V": 2})
def make_confs(self, path_to_work, path_to_equi, refine=False): path_to_work = os.path.abspath(path_to_work) path_to_equi = os.path.abspath(path_to_equi) task_list = [] cwd = os.getcwd() print('gen vacancy with supercell ' + str(self.supercell)) equi_contcar = os.path.join(path_to_equi, 'CONTCAR') if not os.path.exists(equi_contcar): raise RuntimeError("please do relaxation first") ss = Structure.from_file(equi_contcar) vds = VacancyGenerator(ss) dss = [] for jj in vds: dss.append(jj.generate_defect_structure(self.supercell)) if refine: task_list = make_refine(self.parameter['init_from_suffix'], self.parameter['output_suffix'], path_to_work, len(dss)) for ii in task_list: os.chdir(ii) np.savetxt('supercell.out', self.supercell, fmt='%d') os.chdir(cwd) if self.reprod: if 'vasp_path' not in self.parameter: raise RuntimeError( "please provide the vasp_path for reproduction") vasp_path = os.path.abspath(self.parameter['vasp_path']) task_list = reproduce.make_repro(vasp_path, path_to_work) os.chdir(cwd) else: os.chdir(path_to_work) if os.path.isfile('POSCAR'): os.remove('POSCAR') os.symlink(os.path.relpath(equi_contcar), 'POSCAR') # task_poscar = os.path.join(output, 'POSCAR') for ii in range(len(dss)): output_task = os.path.join(path_to_work, 'task.%06d' % ii) os.makedirs(output_task, exist_ok=True) os.chdir(output_task) for jj in [ 'INCAR', 'POTCAR', 'POSCAR', 'conf.lmp', 'in.lammps' ]: if os.path.exists(jj): os.remove(jj) task_list.append(output_task) dss[ii].to('POSCAR', 'POSCAR') np.savetxt('supercell.out', self.supercell, fmt='%d') os.chdir(cwd) return task_list
def make_lammps(jdata, conf_dir, task_type, supercell): 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') assert os.path.exists( equi_contcar), "Please compute the equilibrium state using vasp first" # 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 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) 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-%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'] + 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_vasp(jdata, conf_dir, supercell=[1, 1, 1]): fp_params = jdata['vasp_params'] ecut = fp_params['ecut'] ediff = fp_params['ediff'] npar = fp_params['npar'] kpar = fp_params['kpar'] kspacing = fp_params['kspacing'] kgamma = fp_params['kgamma'] 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') 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, 'vasp-k%.2f' % kspacing) 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 defects vds = VacancyGenerator(ss) dss = [] for jj in vds: dss.append(jj.generate_defect_structure(supercell)) # gen incar fc = vasp.make_vasp_relax_incar(ecut, ediff, True, True, True, npar=npar, kpar=kpar, kspacing=kspacing, kgamma=kgamma) with open(os.path.join(task_path, 'INCAR'), 'w') as fp: fp.write(fc) # gen potcar with open(task_poscar, 'r') as fp: lines = fp.read().split('\n') ele_list = lines[5].split() potcar_map = jdata['potcar_map'] potcar_list = [] for ii in ele_list: assert os.path.exists(os.path.abspath( potcar_map[ii])), "No POTCAR in the potcar_map of %s" % (ii) potcar_list.append(os.path.abspath(potcar_map[ii])) with open(os.path.join(task_path, 'POTCAR'), 'w') as outfile: for fname in potcar_list: with open(fname) as infile: outfile.write(infile.read()) # 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 ['POSCAR', 'POTCAR', 'INCAR']: if os.path.isfile(jj): os.remove(jj) # make conf dss[ii].to('POSCAR', 'POSCAR') # link incar, potcar, kpoints os.symlink(os.path.relpath(os.path.join(task_path, 'INCAR')), 'INCAR') os.symlink(os.path.relpath(os.path.join(task_path, 'POTCAR')), 'POTCAR') # 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 update_vacancy_edges(self, threshold=0.5, batch_size=100, edge_calculator=edg.pairwise_squared_similarity, featurizer=FRAMEWORK_FEATURIZER): ''' solve for directed, boolean edges based on similarity with a vacancy Notes: Transformation limited method (featurization of vacancy structures) Args: threshold (float) distance threshold to connect an edge batch_size (int) batch size for computing pairwise distances when generating graph edges. subject to memory constraints edge_calculator (func) a sub-pairwise distance calculator that returns an N x M adjacency matrix featurizer (BaseFeaturizer) an instance of a structural featurizer ''' # loads a batch of verticies without defined edges self.destination.from_storage( filter={'vacancy_edges': {'$exists': False}}, projection={'material_id': 1}, limit=batch_size) if len(self.destination.memory.index) == 0: return 0 # returns False when update is complete else: # gets the source vertex ids for the current batch source_ids = self.destination.memory['material_id'].values self.destination.memory = None # cleanup memory # gets the potential destination vertex ids and their features self._load_structure_features() all_ids = self.source.memory.index.values all_vectors = self.source.memory.values vector_labels = np.array( [s.split('.')[1] for s in self.source.memory.columns.values]) self.source.memory = None # cleanup memory # calculates feature vectors for each (source) vacancy structure self._load_structures(list(source_ids)) source_structures = self.source.memory['structure'].values self.source.memory = None # cleanup memory vacancy_structures = [] for material_id, structure in zip(source_ids, source_structures): structure = Structure.from_dict(structure) for site_i, vacancy in enumerate(VacancyGenerator(structure)): vacancies = [ material_id, str(site_i), vacancy.generate_defect_structure(supercell=(1, 1, 1)) ] vacancy_structures.append(vacancies) vacancy_structures = DataFrame( data=vacancy_structures, columns=['source_id', 'site_index', 'structure']) vacancy_vectors = featurizer.featurize_dataframe( vacancy_structures, 'structure', ignore_errors=True, pbar=False, inplace=False)[vector_labels].values # determine edge matrix and coresponding adjacency list edge_matrix = edge_calculator( all_vectors, vacancy_vectors, threshold) adjacency_list = defaultdict(dict) for j in range(edge_matrix.shape[1]): source_id = vacancy_structures['source_id'][j] site_index = vacancy_structures['site_index'][j] adjacency_list[source_id][site_index] = list( all_ids[edge_matrix[:, j]]) # store edges in graph space self.destination.memory = DataFrame.from_records( list(adjacency_list.items()), columns=['material_id', 'vacancy_edges']) self.destination.to_storage(identifier='material_id') return 1 # return True to continue the update
def make_confs(self, path_to_work, path_to_equi, refine=False): path_to_work = os.path.abspath(path_to_work) if os.path.exists(path_to_work): dlog.warning('%s already exists' % path_to_work) else: os.makedirs(path_to_work) path_to_equi = os.path.abspath(path_to_equi) if 'start_confs_path' in self.parameter and os.path.exists( self.parameter['start_confs_path']): init_path_list = glob.glob( os.path.join(self.parameter['start_confs_path'], '*')) struct_init_name_list = [] for ii in init_path_list: struct_init_name_list.append(ii.split('/')[-1]) struct_output_name = path_to_work.split('/')[-2] assert struct_output_name in struct_init_name_list path_to_equi = os.path.abspath( os.path.join(self.parameter['start_confs_path'], struct_output_name, 'relaxation', 'relax_task')) task_list = [] cwd = os.getcwd() if self.reprod: print('vacancy reproduce starts') if 'init_data_path' not in self.parameter: raise RuntimeError( "please provide the initial data path to reproduce") init_data_path = os.path.abspath(self.parameter['init_data_path']) task_list = make_repro( init_data_path, self.init_from_suffix, path_to_work, self.parameter.get('reprod_last_frame', False)) os.chdir(cwd) else: if refine: print('vacancy refine starts') task_list = make_refine(self.parameter['init_from_suffix'], self.parameter['output_suffix'], path_to_work) init_from_path = re.sub( self.parameter['output_suffix'][::-1], self.parameter['init_from_suffix'][::-1], path_to_work[::-1], count=1)[::-1] task_list_basename = list(map(os.path.basename, task_list)) for ii in task_list_basename: init_from_task = os.path.join(init_from_path, ii) output_task = os.path.join(path_to_work, ii) os.chdir(output_task) if os.path.isfile('supercell.json'): os.remove('supercell.json') if os.path.islink('supercell.json'): os.remove('supercell.json') os.symlink( os.path.relpath( os.path.join(init_from_task, 'supercell.json')), 'supercell.json') os.chdir(cwd) else: equi_contcar = os.path.join(path_to_equi, 'CONTCAR') if not os.path.exists(equi_contcar): raise RuntimeError("please do relaxation first") ss = Structure.from_file(equi_contcar) vds = VacancyGenerator(ss) dss = [] for jj in vds: dss.append(jj.generate_defect_structure(self.supercell)) print('gen vacancy with supercell ' + str(self.supercell)) os.chdir(path_to_work) if os.path.isfile('POSCAR'): os.remove('POSCAR') if os.path.islink('POSCAR'): os.remove('POSCAR') os.symlink(os.path.relpath(equi_contcar), 'POSCAR') # task_poscar = os.path.join(output, 'POSCAR') for ii in range(len(dss)): output_task = os.path.join(path_to_work, 'task.%06d' % ii) os.makedirs(output_task, exist_ok=True) os.chdir(output_task) for jj in [ 'INCAR', 'POTCAR', 'POSCAR', 'conf.lmp', 'in.lammps' ]: if os.path.exists(jj): os.remove(jj) task_list.append(output_task) dss[ii].to('POSCAR', 'POSCAR') # np.savetxt('supercell.out', self.supercell, fmt='%d') dumpfn(self.supercell, 'supercell.json') os.chdir(cwd) return task_list
def __init__(self, structure, max_min_oxi=None, substitutions=None, oxi_states=None, cellmax=128, antisites_flag=True, include_interstitials=False, interstitial_elements=None, intersites=None, standardized=False, struct_type='semiconductor'): """ Args: structure (Structure): the bulk structure. max_min_oxi (dict): The minimal and maximum oxidation state of each element as a dict. For instance {"O":(-2,0)}. If not given, the oxi-states of pymatgen are considered. substitutions (dict): The allowed substitutions of elements as a dict. If not given, intrinsic defects are computed. If given, intrinsic (e.g., anti-sites) and extrinsic are considered explicitly specified. Example: {"Co":["Zn","Mn"]} means Co sites can be substituted by Mn or Zn. oxi_states (dict): The oxidation state of the elements in the compound e.g. {"Fe":2,"O":-2}. If not given, the oxidation state of each site is computed with bond valence sum. WARNING: Bond-valence method can fail for mixed-valence compounds. cellmax (int): Maximum number of atoms allowed in the supercell. antisites_flag (bool): If False, don't generate antisites. include_interstitials (bool): If true, do generate interstitial defect configurations (default: False). interstitial_elements ([str]): List of strings containing symbols of the elements that are to be considered for interstitial sites. The default (None) triggers self-interstitial generation, given that include_interstitials is True. intersites ([PeriodicSite]): A list of PeriodicSites in the bulk structure on which we put interstitials. Note that you still have to set flag include_interstitials to True in order to make use of this manual way of providing interstitial sites. If this is used, then no additional interstitials are generated beyond the list that is provided in intersites. standardized (bool): If True, use the primitive standard structure as unit cell for generating the defect configurations (default is False). The primitive standard structure is obtained from the SpacegroupAnalyzer class with a symprec of 0.01. struct_type (string): Options are 'semiconductor' and 'insulator'. If semiconductor is selected, charge states based on database of semiconductors is used to assign defect charges. For insulators, defect charges are conservatively assigned. """ max_min_oxi = max_min_oxi if max_min_oxi is not None else {} substitutions = substitutions if substitutions is not None else {} oxi_states = oxi_states if oxi_states is not None else {} interstitial_elements = interstitial_elements if interstitial_elements is not None else [] intersites = intersites if intersites is not None else [] self.defects = [] self.cellmax = cellmax self.substitutions = {} self.struct_type = struct_type for key, val in substitutions.items(): self.substitutions[key] = val spa = SpacegroupAnalyzer(structure, symprec=1e-2) prim_struct = spa.get_primitive_standard_structure() if standardized: self.struct = prim_struct else: self.struct = structure struct_species = self.struct.types_of_specie if self.struct_type == 'semiconductor': self.defect_charger = DefectChargerSemiconductor( self.struct, min_max_oxi=max_min_oxi) elif self.struct_type == 'insulator': self.defect_charger = DefectChargerInsulator(self.struct) elif self.struct_type == 'manual': self.defect_charger = DefectChargerUserCustom( self.struct, oxi_states=oxi_states) elif self.struct_type == 'ionic': self.defect_charger = DefectChargerIonic(self.struct) else: raise NotImplementedError if include_interstitials and interstitial_elements: for elem_str in interstitial_elements: if not Element.is_valid_symbol(elem_str): raise ValueError("invalid interstitial element" " \"{}\"".format(elem_str)) sc_scale = get_optimized_sc_scale(self.struct, cellmax) self.defects = {} sc = self.struct.copy() sc.make_supercell(sc_scale) self.defects['bulk'] = { 'name': 'bulk', 'supercell': { 'size': sc_scale, 'structure': sc } } # If interstitials are provided as a list of PeriodicSites, # make sure that the lattice has not changed. if include_interstitials and intersites: for intersite in intersites: #list of PeriodicSite objects if intersite.lattice != self.struct.lattice: raise RuntimeError( "Discrepancy between lattices" " underlying the input interstitials and" " the bulk structure; possibly because of" " standardizing the input structure.") vacancies = [] as_defs = [] sub_defs = [] VG = VacancyGenerator(self.struct) print("Setting up defects...") for i, vac in enumerate(VG): vac_site = vac.site vac_symbol = vac.site.specie.symbol vac_sc = vac.generate_defect_structure(sc_scale) #create a trivial defect structure to find where supercell transformation moves the lattice struct_for_defect_site = Structure( vac.bulk_structure.copy().lattice, [vac.site.specie], [vac.site.frac_coords], to_unit_cell=True, coords_are_cartesian=False) struct_for_defect_site.make_supercell(sc_scale) vac_sc_site = struct_for_defect_site[0] charges_vac = self.defect_charger.get_charges( 'vacancy', vac_symbol) vacancies.append({ 'name': "vac_{}_{}".format(i + 1, vac_symbol), 'unique_site': vac_site, 'bulk_supercell_site': vac_sc_site, 'defect_type': 'vacancy', 'site_specie': vac_symbol, 'site_multiplicity': vac.multiplicity, 'supercell': { 'size': sc_scale, 'structure': vac_sc }, 'charges': charges_vac }) if antisites_flag: for as_specie in set(struct_species): SG = SubstitutionGenerator(self.struct, as_specie) for i, sub in enumerate(SG): as_symbol = as_specie.symbol as_sc = sub.generate_defect_structure(sc_scale) # create a trivial defect structure to find where supercell transformation moves the defect struct_for_defect_site = Structure( sub.bulk_structure.copy().lattice, [sub.site.specie], [sub.site.frac_coords], to_unit_cell=True, coords_are_cartesian=False) struct_for_defect_site.make_supercell(sc_scale) as_sc_site = struct_for_defect_site[0] #get bulk_site (non sc) poss_deflist = sorted( sub.bulk_structure.get_sites_in_sphere( sub.site.coords, 0.01, include_index=True), key=lambda x: x[1]) if not len(poss_deflist): raise ValueError( "Could not find substitution site inside bulk structure for {}?" .format(sub.name)) defindex = poss_deflist[0][2] as_site = sub.bulk_structure[defindex] vac_symbol = as_site.specie charges_as = self.defect_charger.get_charges( 'antisite', vac_symbol, as_symbol) as_defs.append({ 'name': "as_{}_{}_on_{}".format(i + 1, as_symbol, vac_symbol), 'unique_site': as_site, 'bulk_supercell_site': as_sc_site, 'defect_type': 'antisite', 'site_specie': vac_symbol, 'substitution_specie': as_symbol, 'site_multiplicity': sub.multiplicity, 'supercell': { 'size': sc_scale, 'structure': as_sc }, 'charges': charges_as }) for vac_symbol, subspecie_list in self.substitutions.items(): for subspecie_symbol in subspecie_list: SG = SubstitutionGenerator(self.struct, subspecie_symbol) for i, sub in enumerate(SG): sub_symbol = sub.site.specie.symbol #get bulk_site (non sc) poss_deflist = sorted( sub.bulk_structure.get_sites_in_sphere( sub.site.coords, 0.1, include_index=True), key=lambda x: x[1]) if not len(poss_deflist): raise ValueError( "Could not find substitution site inside bulk structure for {}?" .format(sub.name)) defindex = poss_deflist[0][2] sub_site = self.struct[defindex] this_vac_symbol = sub_site.specie.symbol if (sub_symbol != subspecie_symbol) or (this_vac_symbol != vac_symbol): continue else: sub_sc = sub.generate_defect_structure(sc_scale) # create a trivial defect structure to find where supercell transformation moves the defect struct_for_defect_site = Structure( sub.bulk_structure.copy().lattice, [sub.site.specie], [sub.site.frac_coords], to_unit_cell=True, coords_are_cartesian=False) struct_for_defect_site.make_supercell(sc_scale) sub_sc_site = struct_for_defect_site[0] charges_sub = self.defect_charger.get_charges( 'substitution', vac_symbol, subspecie_symbol) sub_defs.append({ 'name': "sub_{}_{}_on_{}".format(i + 1, subspecie_symbol, vac_symbol), 'unique_site': sub_site, 'bulk_supercell_site': sub_sc_site, 'defect_type': 'substitution', 'site_specie': vac_symbol, 'substitution_specie': subspecie_symbol, 'site_multiplicity': sub.multiplicity, 'supercell': { 'size': sc_scale, 'structure': sub_sc }, 'charges': charges_sub }) self.defects['vacancies'] = vacancies self.defects['substitutions'] = sub_defs self.defects['substitutions'] += as_defs if include_interstitials: interstitials = [] if interstitial_elements: inter_elems = interstitial_elements else: inter_elems = [elem.symbol for elem in \ self.struct.composition.elements] if len(inter_elems) == 0: raise RuntimeError("empty element list for interstitials") if intersites: #manual specification of interstitials for i, intersite in enumerate(intersites): for elt in inter_elems: name = "inter_{}_{}".format(i + 1, elt) if intersite.lattice != self.struct.lattice: err_msg = "Lattice matching error occurs between provided interstitial and the bulk structure." if standardized: err_msg += "\nLikely because the standardized flag was used. Turn this flag off or reset " \ "your interstitial PeriodicSite to match the standardized form of the bulk structure." raise ValueError(err_msg) else: intersite_object = Interstitial( self.struct, intersite) # create a trivial defect structure to find where supercell transformation moves the defect site struct_for_defect_site = Structure( intersite_object.bulk_structure.copy().lattice, [intersite_object.site.specie], [intersite_object.site.frac_coords], to_unit_cell=True, coords_are_cartesian=False) struct_for_defect_site.make_supercell(sc_scale) site_sc = struct_for_defect_site[0] sc_with_inter = intersite_object.generate_defect_structure( sc_scale) charges_inter = self.defect_charger.get_charges( 'interstitial', elt) interstitials.append({ 'name': name, 'unique_site': intersite_object.site, 'bulk_supercell_site': site_sc, 'defect_type': 'interstitial', 'site_specie': intersite_object.site.specie.symbol, 'site_multiplicity': intersite_object.multiplicity, 'supercell': { 'size': sc_scale, 'structure': sc_with_inter }, 'charges': charges_inter }) else: print( "Searching for interstitial sites (this can take awhile)..." ) for elt in inter_elems: #TODO: Add ability to use other interstitial finding methods in pymatgen IG = InterstitialGenerator(self.struct, elt) for i, intersite_object in enumerate(IG): name = intersite_object.name # create a trivial defect structure to find where supercell transformation moves the defect site struct_for_defect_site = Structure( intersite_object.bulk_structure.copy().lattice, [intersite_object.site.specie], [intersite_object.site.frac_coords], to_unit_cell=True, coords_are_cartesian=False) struct_for_defect_site.make_supercell(sc_scale) site_sc = struct_for_defect_site[0] sc_with_inter = intersite_object.generate_defect_structure( sc_scale) charges_inter = self.defect_charger.get_charges( 'interstitial', elt) interstitials.append({ 'name': "inter_{}_{}".format( i + 1, elt), #TODO fix naming convention 'unique_site': intersite_object.site, 'bulk_supercell_site': site_sc, 'defect_type': 'interstitial', 'site_specie': intersite_object.site.specie.symbol, 'site_multiplicity': intersite_object.multiplicity, 'supercell': { 'size': sc_scale, 'structure': sc_with_inter }, 'charges': charges_inter }) self.defects['interstitials'] = interstitials print("\nNumber of jobs created:") tottmp = 0 for j in self.defects.keys(): if j == 'bulk': print(" bulk = 1") tottmp += 1 else: print(" {}:".format(j)) for lis in self.defects[j]: print(" {} = {}".format(lis['name'], len(lis['charges']))) tottmp += len(lis['charges']) print("Total (non dielectric) jobs created = {}\n".format(tottmp))