'''

:param features:

:return: float

'''

# [(bond_A_length - unnormmed_diffusion_distance)]

c = -0.2088636566e1

bond_A_length = get_feat('bond_A_length', features)

unnormmed_diffusion_distance = get_feat('diffusion_distance', features, False)

'''

:param features:

:return: float

'''

# [(is_uncharged * unnormmed_band_gap)]

c = 0.343478247

is_uncharged = get_feat('is_uncharged', features, False)

unnormmed_band_gap = get_feat('band_gap', features, False)

'''

:param features:

:return: float

'''

# [(bond_B_length * unnormmed_diffusion_distance)]

c = - 0.1303360998E+01

bond_B_length = get_feat('bond_B_length', features)

unnormmed_diffusion_distance = get_feat('diffusion_distance', features, False)

'''

:param features:

:return: float

'''

# [(formation_energy + atomization_energy)]

c = 0.2474113532E+01

formation_energy = get_feat('formation_energy', features)

atomization_energy = get_feat('atomization_energy', features)

'Li': -1.90715197,

'Be': -3.771544815,

'C': -9.231242450625,

'O': -4.94442311,

'F': -1.2856885225,

'Na': -1.338663779,

'Mg': -1.5152837411111113,

'Al': -3.74844693,

'Si': -5.424965585,

'P': -5.409410872857143,

'S': -4.126883271875,

'K': -1.0602799475,

'Ca': -1.92335896,

'Sc': -5.04495498,

'Ti': -4.4520116666666665,

'V': -4.26275489,

'Cr': -5.94951935,

'Mn': -6.921141819655173,

'Fe': -5.18751316,

'Co': -4.104854465,

'Ni': -2.27729538,

'Cu': -1.45330976,

'Zn': -1.089421915,

'Ga': -2.91921437,

'Ge': -4.51841983,

'Se': -3.505557640625,

'Sr': -1.63689309,

'Y': -5.31910215,

'Zr': -6.58225052,

'Nb': -7.03852874,

'Cd': -0.736825565,

'In': -2.5518483599999997,

'Sn': -3.84632506,

'Sb': -4.147950705,

'Ba': -1.90871169,

'La': -4.89907303,

'Ce': -4.68467903,

'Pr': -5.357426865,

'Eu': -9.81043707,

'Gd': -13.965768385,

'Lu': -4.415364465,

'Hf': -8.071571385,

'Ta': -9.16697649,

'Li': -0.29803311,

'Be': -0.03839057,

'C': -1.3704101,

'O': -1.90789148,

'F': -0.62474621,

'Na': -0.01109227,

'Mg': -0.00049853,

'Al': -0.14313117,

'Si': -0.62906164,

'P': -0.02135111,

'S': -0.70537265,

'K': -0.17837826,

'Ca': -0.00693727,

'Sc': -1.83549298,

'Ti': -1.87254139,

'V': -2.2389668,

'Cr': -4.64837968,

'Mn': -4.6922531,

'Fe': -2.34548731,

'Co': -0.802624,

'Ni': 0.8892822,

'Cu': 1.5261699,

'Zn': -0.01114151,

'Ga': -0.15173613,

'Ge': -0.0192824,

'Se': -0.89394831,

'Sr': -0.02840861,

'Y': -1.8921745,

'Zr': -1.323768,

'Cd': -0.01418287,

'In': -0.25184654,

'Sn': -0.02986457,

'Sb': -0.87592437,

'Ba': -0.03184493,

'La': -0.61651049,

'Ce': -0.7365616,

'Pr': -2.55308713,

'Nd': -4.44174128,

'Sm': -6.80643846,

'Eu': -8.12423019,

'Gd': -9.92976694,

'Tb': -8.47585603,

'Dy': -7.24110832,

'Ho': -6.69784917,

'Er': -4.9557928,

'Tm': -4.27717282,

'Yb': 0.2062883,

'Lu': -0.21764099,

'Hf': -2.5648182,

'Ta': -2.28858526,

unit_s = poscar.structure

elemental_energies = 0

for atom in unit_s:

element = atom.species_string

elemental_energies += chem_pots[element]

unit_s = poscar.structure

elemental_energies = 0

for atom in unit_s:

element = atom.species_string

elemental_energies += atomization_energies[element]

base_s = poscar.structure

start_i = get_center_i(base_s, Element('O'), )

print(start_i)

vnn = VoronoiNN(targets=[Element(x) for x in elements])

start_vnn = vnn.get_nn_info(base_s, start_i)

weight = {}

total_length = {}

for pt in start_vnn:

# print('{}: {:3.2f}'.format(pt['site_index'], pt['weight']))

temp_weight = round(pt['weight']+0.45)

if pt['site'].species_string in weight:

weight[pt['site'].species_string] = weight[pt['site'].species_string] + temp_weight

else:

weight[pt['site'].species_string] = temp_weight

if temp_weight >= 0.99:

bond_length = base_s.get_distance(start_i, pt['site_index'])

if pt['site'].species_string in total_length:

total_length[pt['site'].species_string] = total_length[pt['site'].species_string] + bond_length

else:

total_length[pt['site'].species_string] = bond_length

bonds = {

'A_Bonds' : weight[elements[0]],

'A_length' : total_length[elements[0]] / weight[elements[0]],

'B_Bonds' : weight[elements[1]],

'B_length' : total_length[elements[1]] / weight[elements[1]],

'avg_length' : (total_length[elements[0]] + total_length[elements[1]]) / (weight[elements[0]] + weight[elements[1]]),

}

base_s = poscar.structure

start_i = get_center_i(base_s, Element('O'))

start_coord = base_s.frac_coords[start_i]

finals = get_vacancy_diffusion_pathways_from_cell(base_s, start_i, get_midpoints=True)[1]

base_s.lattice.get_distance_and_image(start_coord, finals[0])

distances = [2*base_s.lattice.get_distance_and_image(start_coord, final)[0] for final in finals]

# print(distances)

value = features[feat]

if not normalize:

return value

normalization = {}

normalization['bond_A_length'] = (1.8041439076909747, 3.453376021227605)

normalization['bond_B_length'] = (1.6387592735086143, 2.9887241006183083)

normalization['formation_energy'] = (-3.532782558000001, -1.0739940940000003)

normalization['atomization_energy'] = (3.8513157839999996, 6.853106444)

min_value = normalization[feat][0]

max_value = normalization[feat][1]

properties = [

('bond', VoronoiInfo(poscar, vasprun, elements)),

('diffusion_distance', DiffusionDistance(poscar, vasprun, elements)),

('band_gap', Bandgap(poscar, vasprun, elements)),

('formation_energy', FormationEnergy(poscar, vasprun, elements)),

('atomization_energy', AtomizationEnergy(poscar, vasprun, elements)),

]

for name, property in properties:

for feature_set in features.copy():

value = property

if type(value) == dict:

for k in value:

feature_set['{}_{}'.format(name, k)] = value[k]

if type(value) == list: ### Will break for more than one list

features = [copy.deepcopy(feature_set) for _ in range(len(value))]

for i in range(len(value)):

features[i][name] = value[i]

else:

feature_set[name] = value

'''

:param poscar: pymatgen poscar file. Ideally a unit structure

:param vasprun: vasprun corresponding to poscar

:param elements: Elements of the A site and B site in the form [A_site, B_site]. i.e. for BaCoO3 it should be ['Ba', 'Co']. O is not included

:param unchargedP: whether diffusion barriers should be for charged or neutral diffusion

:return: all possible diffusion barriers in the material

'''

features = [{'is_uncharged' : unchargedP}]

feats = [get_feat_1, get_feat_2, get_feat_3, get_feat_4]

barriers = []

features = make_features(features, poscar, vasprun, elements)

for feature_set in features:

barrier=-0.5367985893E+01

for feat in feats:

barrier += feat(feature_set)

barriers.append(barrier)