perovskite_prediction_double.bkr 3.7 MB
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            "view": {
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    "cells": [
        {
            "id": "py-init",
            "type": "code",
            "evaluator": "Python3",
            "input": {
                "body": [
                    "import numpy as np",
                    "import os",
                    "import pandas as pd",
                    "import math",
                    "import re",
                    "from urllib.request import urlopen"
                ],
                "hidden": true
            },
            "output": {
                "state": {},
                "selectedType": "Hidden",
                "pluginName": "Python3",
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        {
            "id": "py-dic",
            "type": "code",
            "evaluator": "Python3",
            "input": {
                "body": [
                    "### make dictionary of Shannon ionic radii",
                    "# starting with table available at v.web.umkc.edu/vanhornj/Radii.xls with Sn2+ added from 10.1039/c5sc04845a",
                    "# and organic cations from 10.1039/C4SC02211D",
                    "if beaker.show_output == 1:",
                    "    import os",
                    "    import json",
                    "fjson = 'Shannon_radii_dict.json'",
                    "if not os.path.exists(fjson):",
                    "    df = pd.read_csv(\"https://gitlab.mpcdf.mpg.de/nomad-lab/nomad-lab-base/raw/master/analysis-tools/perovsktie-predictor/Shannon_Effective_Ionic_Radii.csv\")",
                    "",
                    "    df = df.rename(columns = {'OX. State': 'ox',",
                    "                              'Coord. #': 'coord',",
                    "                              'Crystal Radius': 'rcryst',",
                    "                              'Ionic Radius': 'rion',",
                    "                              'Spin State' : 'spin'})",
                    "",
                    "    df['spin'] = [spin if spin in ['HS', 'LS'] else 'only_spin' for spin in df.spin.values]",
                    "",
                    "    def get_el(row):",
                    "        ION = row['ION']",
                    "        if ' ' in ION:",
                    "            return ION.split(' ')[0]",
                    "        elif '+' in ION:",
                    "            return ION.split('+')[0]",
                    "        elif '-' in ION:",
                    "            return ION.split('-')[0]",
                    "",
                    "    df['el'] = df.apply(lambda row: get_el(row), axis = 1)",
                    "",
                    "    # get allowed oxidation states for each ion",
                    "    el_to_ox = {}",
                    "    for el in df.el.values:",
                    "        el_to_ox[el] = list(set(df.ox.get((df['el'] == el)).tolist()))",
                    "",
                    "    # get ionic radii as function of oxidation state -> coordination number -> spin state",
                    "    Shannon_dict = {}",
                    "    for el in el_to_ox:",
                    "        # list of Shannon oxidation states for each element",
                    "        oxs = el_to_ox[el]",
                    "        ox_to_coord = {}",
                    "        for ox in oxs:",
                    "            # list of coordination numbers for each (element, oxidation state)",
                    "            coords = df.coord.get((df['el'] == el) & (df['ox'] == ox)).tolist()",
                    "            ox_to_coord[ox] = coords",
                    "            coord_to_spin = {}",
                    "            for coord in ox_to_coord[ox]:",
                    "                # list of spin states for each (element, oxidation state, coordination number)",
                    "                spin = df.spin.get((df['el'] == el) & (df['ox'] == ox) & (df['coord'] == coord)).tolist()",
                    "                coord_to_spin[coord] = spin",
                    "                spin_to_rad = {}",
                    "                for spin in coord_to_spin[coord]:",
                    "                    # list of radiis for each (element, oxidation state, coordination number)",
                    "                    rad = df.rion.get((df['el'] == el) & (df['ox'] == ox) & (df['coord'] == coord) & (df['spin'] == spin)).tolist()[0]",
                    "                    spin_to_rad[spin] = rad  ",
                    "                    coord_to_spin[coord] = spin_to_rad",
                    "                    ox_to_coord[ox] = coord_to_spin",
                    "        Shannon_dict[el] = ox_to_coord",
                    "",
                    "    # assign spin state for transition metals (assumes that if an ion can be high-spin, it will be)",
                    "    spin_els = ['Cr', 'Mn', 'Fe', 'Co', 'Ni', 'Cu']",
                    "    starting_d = [4, 5, 6, 7, 8, 9]",
                    "    d_dict = dict(zip(spin_els, starting_d))",
                    "    for el in spin_els:",
                    "        for ox in Shannon_dict[el].keys():",
                    "            for coord in Shannon_dict[el][ox].keys():",
                    "                if len(Shannon_dict[el][ox][coord].keys()) > 1:",
                    "                    num_d = d_dict[el] + 2 - ox",
                    "                    if num_d in [4, 5, 6, 7]:",
                    "                        Shannon_dict[el][ox][coord]['only_spin'] = Shannon_dict[el][ox][coord]['HS']",
                    "                    else:",
                    "                        Shannon_dict[el][ox][coord]['only_spin'] = Shannon_dict[el][ox][coord]['LS']",
                    "                elif 'HS' in Shannon_dict[el][ox][coord].keys():",
                    "                    Shannon_dict[el][ox][coord]['only_spin'] = Shannon_dict[el][ox][coord]['HS']",
                    "                elif 'LS' in Shannon_dict[el][ox][coord].keys():",
                    "                    Shannon_dict[el][ox][coord]['only_spin'] = Shannon_dict[el][ox][coord]['LS']",
                    "    with open(fjson, 'w') as f:",
                    "        json.dump(Shannon_dict, f)",
                    "else:",
                    "    with open(fjson) as f:",
                    "        Shannon_dict = json.load(f)",
                    "        for k1 in Shannon_dict:",
                    "            k2s = list(Shannon_dict[k1].keys())",
                    "            for k2 in k2s:",
                    "                k3s = list(Shannon_dict[k1][k2].keys())",
                    "                Shannon_dict[k1][int(k2)] = Shannon_dict[k1][k2]",
                    "                del Shannon_dict[k1][k2]",
                    "                for k3 in k3s:",
                    "                    Shannon_dict[k1][int(k2)][int(k3)] = Shannon_dict[k1][int(k2)][k3]",
                    "                    del Shannon_dict[k1][int(k2)][k3]  ",
                    "                ",
                    ""
                ],
                "hidden": true
            },
            "output": {
                "state": {},
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            "id": "py-predict",
            "type": "code",
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            "input": {
                "body": [
                    "if beaker.show_output == 0:",
                    "    print('calculating...\\n')",
                    "if beaker.show_output == 1:",
                    "    import pickle",
                    "    import numpy as np",
                    "    import pandas as pd",
                    "    import math",
                    "    import re",
                    "    from sklearn.calibration import CalibratedClassifierCV",
                    "    from itertools import combinations, product",
                    "    from math import gcd",
                    "    import os",
                    "",
                    "",
                    "class PredictABX3(object):",
                    "    \"\"\"",
                    "    for undoped ABX3s",
                    "        -predicts which cation is A or B",
                    "        -determines whether compound can be charge-balanced",
                    "        -assigns oxidation states for A and B",
                    "        -predicts radii",
                    "        -generates t and tau",
                    "        -classifies as perovskite/nonperovskite based on t and tau",
                    "        -generates tau-derived probability of stability in the perovskite structure",
                    "    \"\"\"",
                    "    ",
                    "    def __init__(self, initial_form):",
                    "        \"\"\"",
                    "        Args:",
                    "            initial_form (str) - CC'X3 to classify",
                    "        \"\"\"",
                    "        self.initial_form = initial_form",
                    "    ",
                    "    @property",
                    "    def els(self):",
                    "        \"\"\"",
                    "        list of elements in formula (str)",
                    "        \"\"\"",
                    "        return re.findall('[A-Z][a-z]?', self.initial_form)",
                    "    ",
                    "    @property",
                    "    def X(self):",
                    "        \"\"\"",
                    "        anion (str)",
                    "        \"\"\"",
                    "        el_num_pairs = [[el_num_pair[idx] for idx in range(len(el_num_pair)) if el_num_pair[idx] != ''][0]",
                    "                                  for el_num_pair in re.findall('([A-Z][a-z]\\d*)|([A-Z]\\d*)', self.initial_form)]",
                    "        return [el_num_pair.replace('3', '') for el_num_pair in el_num_pairs if '3' in el_num_pair][0]",
                    "    ",
                    "    @property",
                    "    def cations(self):",
                    "        \"\"\"",
                    "        list of cations (str)",
                    "        \"\"\"",
                    "        els = self.els",
                    "        return [el for el in els if el != self.X]",
                    "        ",
                    "    @property",
                    "    def X_ox_dict(self):",
                    "        \"\"\"",
                    "        returns {el (str): oxidation state (int)} for allowed anions",
                    "        \"\"\"",
                    "        return {'N' : -3,",
                    "                'O' : -2,",
                    "                'S' : -2,",
                    "                'Se' : -2,",
                    "                'F' : -1,",
                    "                'Cl' : -1,",
                    "                'Br' : -1,",
                    "                'I' : -1}",
                    "    ",
                    "    @property",
                    "    def plus_one(self):",
                    "        \"\"\"",
                    "        returns list of elements (str) likely to be 1+",
                    "        \"\"\"",
                    "        return ['H', 'Li', 'Na', 'K', 'Rb', 'Cs', 'Fr', 'Ag']",
                    "    ",
                    "    @property",
                    "    def plus_two(self):",
                    "        \"\"\"",
                    "        returns list of elements (str) likely to be 2+",
                    "        \"\"\"        ",
                    "        return ['Be', 'Mg', 'Ca', 'Sr', 'Ba', 'Ra']",
                    "    ",
                    "    @property",
                    "    def plus_three(self):",
                    "        \"\"\"",
                    "        returns list of elements (str) likely to be 3+",
                    "        \"\"\"        ",
                    "        return ['Sc', 'Y', 'La', 'Al', 'Ga', 'In',",
                    "                'Pr', 'Nd', 'Pm', 'Sm', 'Eu', 'Gd', 'Tb',",
                    "                'Dy', 'Ho', 'Er', 'Tm', 'Yb', 'Lu']    ",
                    "            ",
                    "    @property",
                    "    def chi_dict(self):",
                    "        \"\"\"",
                    "        returns {el (str) : Pauling electronegativity (float)} for cations",
                    "        \"\"\"",
                    "        cations = self.cations",
                    "        chi_dict = {}",
                    "        df1 = pd.read_csv(\"https://gitlab.mpcdf.mpg.de/nomad-lab/nomad-lab-base/raw/master/analysis-tools/perovsktie-predictor/electronegativities.csv\")",
                    "        tmp_cation = []",
                    "        tmp_cation.append(df1.columns.tolist()[0])",
                    "        tmp_cation.extend(df1[df1.columns.tolist()[0]])",
                    "        tmp_el = []",
                    "        tmp_el.append( df1.columns.tolist()[1] )",
                    "        tmp_el.extend( df1[df1.columns.tolist()[1]] )",
                    "        df2 = pd.DataFrame({\"cation\":tmp_cation, ",
                    "                     \"el\":tmp_el})",
                    "        # generate a dictionary of {cation : electronegativity} for help with assignment",
                    "        for cation in cations:",
                    "            idx = df2.index[df2['cation'] == cation].tolist()",
                    "            chi_dict[df2.loc[idx].cation.values[0]] = float(df2.loc[idx].el.values[0])",
                    "        return chi_dict",
                    "    ",
                    "    @property",
                    "    def allowed_ox(self):",
                    "        \"\"\"",
                    "        returns {el (str) : list of allowed oxidation states (int)} for each ion",
                    "        \"\"\"",
                    "        X = self.X",
                    "        cations = self.cations",
                    "        allowed_ox_dict = {}",
                    "        for cation in cations:",
                    "            # if cation is commonly 1+, make that the only allowed oxidation state",
                    "            if cation in self.plus_one:",
                    "                allowed_ox_dict[cation] = [1]",
                    "            # if cation is commonly 2+, make that the only allowed oxidation state",
                    "            elif cation in self.plus_two:",
                    "                allowed_ox_dict[cation] = [2]",
                    "            # otherwise, use the oxidation states that have corresponding Shannon radii                ",
                    "            else:",
                    "                allowed_ox_dict[cation] = [val for val in list(Shannon_dict[cation].keys()) if val > 0]",
                    "        allowed_ox_dict[X] = [self.X_ox_dict[X]]",
                    "        return allowed_ox_dict",
                    "    ",
                    "    @property",
                    "    def charge_bal_combos(self):",
                    "        \"\"\"",
                    "        returns list of oxidation state pairs (tuple of ints) which charge-balance X3",
                    "        \"\"\"",
                    "        cations = self.cations",
                    "        X = self.X",
                    "        allowed_ox = self.allowed_ox",
                    "        ox1s = allowed_ox[cations[0]]",
                    "        ox2s = allowed_ox[cations[1]]",
                    "        oxX = allowed_ox[X][0]",
                    "        bal_combos = []",
                    "        for ox1 in ox1s:",
                    "            for ox2 in ox2s:",
                    "                if ox1 + ox2 == -3*oxX:",
                    "                    bal_combos.append((ox1, ox2))",
                    "        if len(bal_combos) == 0:",
                    "            #print(self.initial_form)",
                    "            #print('No charge balanced combinations. . .')",
                    "            return np.nan",
                    "        else:",
                    "            return bal_combos ",
                    "    ",
                    "    @property",
                    "    def chosen_ox_states(self):",
                    "        \"\"\"",
                    "        returns {el (str) : assigned oxidation state (int)} for cations",
                    "        \"\"\"",
                    "        combos = self.charge_bal_combos",
                    "        if isinstance(combos, float):",
                    "            return np.nan",
                    "        chi_dict = self.chi_dict",
                    "        cations = self.cations",
                    "        X = self.X",
                    "        plus_three = self.plus_three",
                    "        # if only one charge-balanced combination exists, use it",
                    "        if len(combos) == 1:",
                    "            ox_states = dict(zip(cations, combos[0]))",
                    "        # if two combos exists and they are the reverse of one another",
                    "        elif (len(combos) == 2) and (combos[0] == combos[1][::-1]):",
                    "            # assign the minimum oxidation state to the more electronegative cation",
                    "            min_ox = np.min(combos[0])",
                    "            max_ox = np.max(combos[1])",
                    "            epos_el = [el for el in cations if chi_dict[el] == np.min(list(chi_dict.values()))][0]",
                    "            eneg_el = [el for el in cations if el != epos_el][0]",
                    "            ox_states = {epos_el : max_ox,",
                    "                         eneg_el : min_ox}",
                    "        else:",
                    "            # if one of the cations is probably 3+, let it be 3+",
                    "            if (cations[0] in plus_three) or (cations[1] in plus_three):",
                    "                if X == 'O':",
                    "                    if (3,3) in combos:",
                    "                        combo = (3,3)",
                    "                        ox_states = dict(zip(cations, list(combo)))",
                    "            # else compare electronegativities - if 0.9 < chi1/chi2 < 1.1, minimize the oxidation state diff",
                    "            elif np.min(list(chi_dict.values())) > 0.9 * np.max(list(chi_dict.values())):",
                    "                diffs = [abs(combo[0] - combo[1]) for combo in combos]",
                    "                mindex = [idx for idx in range(len(diffs)) if diffs[idx] == np.min(diffs)]",
                    "                if len(mindex) == 1:",
                    "                    mindex = mindex[0]",
                    "                    combo = combos[mindex]",
                    "                    ox_states = dict(zip(cations, combo))",
                    "                else:",
                    "                    min_ox = np.min([combos[idx] for idx in mindex])",
                    "                    max_ox = np.max([combos[idx] for idx in mindex])",
                    "                    epos_el = [el for el in cations if chi_dict[el] == np.min(list(chi_dict.values()))][0]",
                    "                    eneg_el = [el for el in cations if el != epos_el][0]",
                    "                    ox_states = {epos_el : max_ox,",
                    "                                 eneg_el : min_ox} ",
                    "            else:",
                    "                diffs = [abs(combo[0] - combo[1]) for combo in combos]",
                    "                maxdex = [idx for idx in range(len(diffs)) if diffs[idx] == np.max(diffs)]",
                    "                if len(maxdex) == 1:",
                    "                    maxdex = maxdex[0]",
                    "                    combo = combos[maxdex]",
                    "                    ox_states = dict(zip(cations, combo))",
                    "                else:",
                    "                    min_ox = np.min([combos[idx] for idx in maxdex])",
                    "                    max_ox = np.max([combos[idx] for idx in maxdex])",
                    "                    epos_el = [el for el in cations if chi_dict[el] == np.min(list(chi_dict.values()))][0]",
                    "                    eneg_el = [el for el in cations if el != epos_el][0]",
                    "                    ox_states = {epos_el : max_ox,",
                    "                                eneg_el : min_ox}",
                    "        return ox_states",
                    "    ",
                    "    @property",
                    "    def AB_radii_dict(self):",
                    "        \"\"\"",
                    "        returns {el (str) : {'A_rad' : radius if A (float),",
                    "                             'B_rad' : radius if B (float)}}",
                    "        \"\"\"",
                    "        ox_dict = self.chosen_ox_states",
                    "        if isinstance(ox_dict, float):",
                    "            return np.nan",
                    "        radii_dict = {}",
                    "        for el in ox_dict:",
                    "            tmp_dict = {}",
                    "            # get the oxidation state",
                    "            ox = ox_dict[el]",
                    "            coords = list(Shannon_dict[el][ox].keys())",
                    "            # get the B CN as the one available nearest 6",
                    "            B_coords = [abs(coord - 6) for coord in coords]",
                    "            mindex = [idx for idx in range(len(B_coords)) if B_coords[idx] == np.min(B_coords)][0]",
                    "            B_coord = coords[mindex]",
                    "            # get the A CN as the one available nearest 12",
                    "            A_coords = [abs(coord - 12) for coord in coords]",
                    "            mindex = [idx for idx in range(len(A_coords)) if A_coords[idx] == np.min(A_coords)][0]",
                    "            A_coord = coords[mindex]",
                    "            # produce the equivalent B-site and A-site radii",
                    "            B_rad = Shannon_dict[el][ox][B_coord]['only_spin']",
                    "            A_rad = Shannon_dict[el][ox][A_coord]['only_spin']",
                    "            tmp_dict['A_rad'] = A_rad",
                    "            tmp_dict['B_rad'] = B_rad",
                    "            radii_dict[el] = tmp_dict",
                    "        return radii_dict",
                    "",
                    "    @property",
                    "    def pred_A(self):",
                    "        \"\"\"",
                    "        returns predicted A (str)",
                    "        \"\"\"",
                    "        ox_dict = self.chosen_ox_states",
                    "        if isinstance(ox_dict, float):",
                    "            return np.nan        ",
                    "        radii_dict = self.AB_radii_dict",
                    "        el1 = list(radii_dict.keys())[0]",
                    "        el2 = list(radii_dict.keys())[1]",
                    "        if (radii_dict[el1]['A_rad'] > radii_dict[el2]['B_rad']) and (radii_dict[el1]['B_rad'] > radii_dict[el2]['A_rad']):",
                    "            return el1",
                    "        elif (radii_dict[el1]['A_rad'] < radii_dict[el2]['B_rad']) and (radii_dict[el1]['B_rad'] < radii_dict[el2]['A_rad']):",
                    "            return el2",
                    "        elif (radii_dict[el1]['A_rad'] > radii_dict[el2]['A_rad']) and (radii_dict[el1]['B_rad'] > radii_dict[el2]['B_rad']):",
                    "            return el1",
                    "        elif (radii_dict[el1]['A_rad'] < radii_dict[el2]['A_rad']) and (radii_dict[el1]['B_rad'] < radii_dict[el2]['B_rad']):",
                    "            return el2",
                    "        elif (radii_dict[el1]['B_rad'] > radii_dict[el2]['B_rad']):",
                    "            return el1",
                    "        elif (radii_dict[el1]['B_rad'] < radii_dict[el2]['B_rad']):",
                    "            return el2",
                    "        elif (radii_dict[el1]['A_rad'] > radii_dict[el2]['A_rad']):",
                    "            return el1",
                    "        elif (radii_dict[el1]['A_rad'] < radii_dict[el2]['A_rad']):",
                    "            return el2  ",
                    "        else:",
                    "            if ox_dict[el1] < ox_dict[el2]:",
                    "                return el1",
                    "            else:",
                    "                return el2",
                    "    ",
                    "    @property",
                    "    def pred_B(self):",
                    "        \"\"\"",
                    "        returns predicted B (str)",
                    "        \"\"\"",
                    "        cations = self.cations",
                    "        pred_A = self.pred_A",
                    "        if pred_A in cations:",
                    "            return [cation for cation in cations if cation != pred_A][0]",
                    "        else:",
                    "            return np.nan",
                    "    ",
                    "    @property",
                    "    def nA(self):",
                    "        \"\"\"",
                    "        returns oxidation state assigned to A (int)",
                    "        \"\"\"",
                    "        if isinstance(self.chosen_ox_states, float):",
                    "            return np.nan",
                    "        else:",
                    "            return self.chosen_ox_states[self.pred_A]",
                    "    ",
                    "    @property",
                    "    def nB(self):",
                    "        \"\"\"",
                    "        returns oxidation state assigned to B (int)",
                    "        \"\"\"",
                    "        if isinstance(self.chosen_ox_states, float):",
                    "            return np.nan",
                    "        else:        ",
                    "            return self.chosen_ox_states[self.pred_B]",
                    "        ",
                    "    @property",
                    "    def nX(self):",
                    "        \"\"\"",
                    "        returns oxidation state assigned to X (int)",
                    "        \"\"\"",
                    "        if isinstance(self.chosen_ox_states, float):",
                    "            return np.nan",
                    "        else:        ",
                    "            return self.X_ox_dict[self.X]",
                    "    ",
                    "    @property",
                    "    def rA(self):",
                    "        \"\"\"",
                    "        returns predicted Shannon ionic radius for A (float)",
                    "        \"\"\"",
                    "        if isinstance(self.AB_radii_dict, float):",
                    "            return np.nan",
                    "        else:        ",
                    "            return self.AB_radii_dict[self.pred_A]['A_rad']",
                    "    ",
                    "    @property",
                    "    def rB(self):",
                    "        \"\"\"",
                    "        returns predicted Shannon ionic radius for B (float)",
                    "        \"\"\"",
                    "        if isinstance(self.AB_radii_dict, float):",
                    "            return np.nan",
                    "        else:      ",
                    "            return self.AB_radii_dict[self.pred_B]['B_rad']",
                    "    ",
                    "    @property",
                    "    def rX(self):",
                    "        \"\"\"",
                    "        returns Shannon ionic radius for X (float)",
                    "        \"\"\"",
                    "        return Shannon_dict[self.X][self.X_ox_dict[self.X]][6]['only_spin']",
                    "    ",
                    "    @property",
                    "    def mu(self):",
                    "        \"\"\"",
                    "        returns the predicted octahedral factor (float)",
                    "        \"\"\"",
                    "        if isinstance(self.AB_radii_dict, float):",
                    "            return np.nan",
                    "        else:          ",
                    "            return self.rB / self.rX",
                    "    ",
                    "    @property",
                    "    def t(self):",
                    "        \"\"\"",
                    "        returns the predicted Goldschmidt tolerance factor (float)",
                    "        \"\"\"",
                    "        if isinstance(self.AB_radii_dict, float):",
                    "            return np.nan",
                    "        else:          ",
                    "            return (self.rA + self.rX) / (np.sqrt(2) * (self.rB + self.rX))",
                    "        ",
                    "    @property",
                    "    def tau(self):",
                    "        \"\"\"",
                    "        returns tau (float)",
                    "        \"\"\"",
                    "        if isinstance(self.AB_radii_dict, float):",
                    "            return np.nan",
                    "        else:",
                    "            try:",
                    "                return ((1/self.mu) - (self.nA)**2 + (self.nA) * (self.rA/self.rB)/(np.log(self.rA/self.rB)))",
                    "            except:",
                    "                return np.nan",
                    "    ",
                    "    @property",
                    "    def tau_pred(self):",
                    "        \"\"\"",
                    "        returns prediction of 1 (perovskite) or -1 (nonperovskite) based on tau",
                    "        \"\"\"",
                    "        if math.isnan(self.tau):",
                    "            return np.nan",
                    "        else:",
                    "            return [1 if self.tau < 4.18 else -1][0]",
                    "        ",
                    "    @property",
                    "    def t_pred(self):",
                    "        \"\"\"",
                    "        returns prediction of 1 (perovskite) or -1 (nonperovskite) based on t",
                    "        \"\"\"        ",
                    "        if math.isnan(self.t):",
                    "            return np.nan",
                    "        else:",
                    "            return [1 if (self.t > 0.825) and (self.t < 1.059) else -1][0]  ",
                    "        ",
                    "    @property",
                    "    def calibrate_tau(self):",
                    "        path_to_save = \"https://gitlab.mpcdf.mpg.de/nomad-lab/nomad-lab-base/raw/master/analysis-tools/perovsktie-predictor/\"",
                    "        f_clf = 'save_clf.p'",
                    "        if not os.path.exists(f_clf):",
                    "            print('creating classifier')",
                    "            np.random.seed(123)                  ",
                    "            df = pd.read_csv(os.path.join(path_to_save, 'TableS1.csv'))",
                    "            #df = pd.read_csv('TableS1.csv')",
                    "            #df = df.sample(n=20)",
                    "            #df['tau'] = [PredictABX3(ABX3).tau for ABX3 in df.ABX3.values]",
                    "            X, y = df['tau'].values.reshape(-1, 1), df['exp_label'].values",
                    "            clf = CalibratedClassifierCV(cv=3)",
                    "            clf.fit(X, y)",
                    "            pickle.dump( clf, open(f_clf, \"wb\" )  )",
                    "            print('classifier created')",
                    "            return clf",
                    "        else:",
                    "            return pickle.load(open(f_clf, \"rb\" ))        ",
                    "        ",
                    "        ",
                    "        ",
                    "#        import pickle",
                    "#        \"\"\"",
                    "#        returns a calibrated classifier to yield tau probabilities",
                    "#        \"\"\"",
                    "#        df = pd.read_csv(\"https://gitlab.mpcdf.mpg.de/nomad-lab/nomad-lab-base/raw/master/analysis-tools/perovsktie-predictor/TableS1.csv\")",
                    "#        #df = pd.read_csv('TableS1.csv')",
                    "#        #df = df.sample(n=20)",
                    "#        df['tau'] = [PredictABX3(ABX3).tau for ABX3 in df.ABX3.values]",
                    "#        X, y = df['tau'].values.reshape(-1, 1), df['exp_label'].values",
                    "#        clf = CalibratedClassifierCV(cv=3)",
                    "#        clf.fit(X, y)",
                    "#        pickle.dump( clf, open( \"save.p\", \"wb\" )  )",
                    "#        return clf",
                    "    ",
                    "    @property",
                    "    def tau_prob(self, clf):",
                    "        \"\"\"",
                    "        Args:",
                    "            clf (sklearn object) - calibrated classifier based on tau",
                    "        Returns:",
                    "            probability of perovskite based on tau (float)",
                    "        \"\"\"",
                    "        X = [[self.tau]]",
                    "        return clf.predict_proba(X)[0][1]",
                    "    ",
                    "    ",
                    "    ",
                    "class PredictAABBXX6(object):",
                    "    \"\"\"",
                    "    classifies the following compounds:",
                    "        -ABX3 (defaults to PredictABX3(CC'X3))",
                    "        -A2BB'X6",
                    "        -AA'B2X6",
                    "        -A2B2(XX')6",
                    "        -AA'BB'X6",
                    "        -AA'B2(XX')6",
                    "        -A2BB'(XX')6",
                    "        -AA'BB'(XX')6",
                    "    \"\"\"",
                    "    ",
                    "    def __init__(self, A1, A2, B1, B2, X1, X2):",
                    "        \"\"\"",
                    "        Args:",
                    "            A1 (str) - element A",
                    "            A2 (str) - element A' if applicable, otherwise A",
                    "            B1 (str) - element B",
                    "            B2 (str) - element B' if applicable, otherwise B",
                    "            X1 (str) - element X",
                    "            X2 (str) - element X' if applicable, otherwise X           ",
                    "        \"\"\"",
                    "        self.A1 = A1",
                    "        self.A2 = A2",
                    "        self.B1 = B1",
                    "        self.B2 = B2",
                    "        self.X1 = X1",
                    "        self.X2 = X2",
                    "        ",
                    "    @property",
                    "    def is_single(self):",
                    "        if (self.A1 == self.A2) and (self.B1 == self.B2) and (self.X1 == self.X2):",
                    "            return 1",
                    "        else:",
                    "            return -1",
                    "        ",
                    "    @property",
                    "    def A(self):",
                    "        if self.is_single == 1:",
                    "            return PredictABX3(self.good_form).pred_A",
                    "        else:",
                    "            return np.nan",
                    "        ",
                    "    @property",
                    "    def B(self):",
                    "        if self.is_single == 1:",
                    "            return PredictABX3(self.good_form).pred_B",
                    "        else:",
                    "            return np.nan",
                    "        ",
                    "    @property",
                    "    def As(self):",
                    "        \"\"\"",
                    "        returns list of A cations (str)",
                    "        \"\"\"",
                    "        return list(set([self.A1, self.A2]))",
                    "    ",
                    "    @property",
                    "    def Bs(self):",
                    "        \"\"\"",
                    "        returns list of B cations (str)",
                    "        \"\"\"        ",
                    "        return list(set([self.B1, self.B2]))",
                    "    ",
                    "    @property",
                    "    def Xs(self):",
                    "        \"\"\"",
                    "        returns list of X anions (str)",
                    "        \"\"\"        ",
                    "        return list(set([self.X1, self.X2]))",
                    "    ",
                    "    @property",
                    "    def X(self):",
                    "        if self.is_single == 1:",
                    "            return self.Xs[0]",
                    "        else:",
                    "            return self.Xs",
                    "    ",
                    "    @property",
                    "    def els(self):",
                    "        \"\"\"",
                    "        returns list of elements (str) in As, Bs, Xs order",
                    "        \"\"\"        ",
                    "        return self.As + self.Bs + self.Xs",
                    "    ",
                    "    @property",
                    "    def formula(self):",
                    "        \"\"\"",
                    "        returns pretty chemical formula in AA'BB'X3X'3 format (str)",
                    "        \"\"\"",
                    "        if len(self.As) == 1:",
                    "            A_piece = ''.join([self.As[0], '2'])",
                    "        else:",
                    "            A_piece = ''.join(self.As)",
                    "        if len(self.Bs) == 1:",
                    "            B_piece = ''.join([self.Bs[0], '2'])",
                    "        else:",
                    "            B_piece = ''.join(self.Bs)",
                    "        if len(self.Xs) == 1:",
                    "            X_piece = ''.join([self.Xs[0], '6'])",
                    "        else:",
                    "            X_piece = ''.join([self.Xs[0], '3', self.Xs[1], '3'])",
                    "        return ''.join([A_piece, B_piece, X_piece])",
                    "    ",
                    "    @property",
                    "    def good_form(self):",
                    "        \"\"\"",
                    "        returns standard formula (str); alphabetized, \"1s\", etc.",
                    "        \"\"\"",
                    "        el_num_pairs = re.findall('([A-Z][a-z]\\d*)|([A-Z]\\d*)', self.formula)",
                    "        el_num_pairs = [[pair[idx] for idx in range(len(pair))if pair[idx] != ''][0] for pair in el_num_pairs]",
                    "        el_num_pairs = [pair+'1' if bool(re.search(re.compile('\\d'), pair)) == False else pair for pair in el_num_pairs]",
                    "        el_num_pairs = sorted(el_num_pairs)",
                    "        formula = ''.join(el_num_pairs)",
                    "        nums = list(map(int, re.findall('\\d+', formula)))",
                    "        if 1 not in nums:",
                    "            names = re.findall('[A-Z][a-z]?', formula)        ",
                    "            combos = list(combinations(nums, 2))",
                    "            factors = [gcd(combo[0], combo[1]) for combo in combos]",
                    "            gcf = np.min(factors)",
                    "            new_nums = [int(np.round(num/gcf)) for num in nums]        ",
                    "            el_num_pairs = []",
                    "            for idx in range(len(names)):",
                    "                el_num_pairs.append(''.join([names[idx], str(new_nums[idx])]))",
                    "            el_num_pairs = [str(pair) for pair in el_num_pairs]",
                    "            return ''.join(sorted(el_num_pairs))  ",
                    "        else:",
                    "            return formula",
                    "    ",
                    "    @property",
                    "    def atom_names(self):",
                    "        \"\"\"",
                    "        returns alphabetical list (str) of atomic symbols in composition",
                    "        e.g., good_form = 'Al2O3', atom_names = ['Al','O']",
                    "        \"\"\"",
                    "        return re.findall('[A-Z][a-z]?', self.good_form)",
                    "    ",
                    "    @property",
                    "    def atom_nums(self):",
                    "        \"\"\"",
                    "        returns list (int) corresponding with number of each element in composition",
                    "            order of list corresponds with alphabetized atomic symbols in composition",
                    "        e.g., good_form = 'Al2O3', atom_nums = [2, 3]",
                    "        \"\"\"",
                    "        return list(map(int, re.findall('\\d+', self.good_form)))",
                    "    ",
                    "    @property",
                    "    def frac_atom_nums(self):",
                    "        \"\"\"",
                    "        returns list (float) of mol fraction of each element in composition",
                    "            order of list corresponds with alphabetized atomic symbols in composition",
                    "        e.g., good_form = 'Al2O3', frac_atom_nums = [0.4, 0.6]",
                    "        \"\"\"",
                    "        atom_nums = self.atom_nums",
                    "        num_atoms = self.num_atoms",
                    "        return [num / num_atoms for num in atom_nums]",
                    "",
                    "    @property",
                    "    def conc_dict(self):",
                    "        \"\"\"",
                    "        returns dictionary of {el (str) : concentration in AxA'1-xByB'1-yXzX'3-z format (float)}",
                    "        \"\"\"",
                    "        els = self.atom_names",
                    "        conc = self.frac_atom_nums",
                    "        natoms = self.num_atoms",
                    "        return {els[idx] : conc[idx] *natoms/2 for idx in range(len(els))}",
                    "",
                    "    @property",
                    "    def num_els(self):",
                    "        \"\"\"",
                    "        returns how many unique elements in composition (int)",
                    "        e.g., good_form = 'Al2O3', num_els = 2",
                    "        \"\"\"",
                    "        return len(self.atom_names)",
                    "",
                    "    @property",
                    "    def num_atoms(self):",
                    "        \"\"\"",
                    "        returns how many atoms in composition (int)",
                    "        e.g., good_form = 'Al2O3', num_atoms = 5",
                    "        \"\"\"",
                    "        return np.sum(self.atom_nums)    ",
                    "    ",
                    "    @property",
                    "    def X_ox_dict(self):",
                    "        \"\"\"",
                    "        returns {el (str): oxidation state (int)} for allowed anions",
                    "        \"\"\"",
                    "        return {'N' : -3,",
                    "                'O' : -2,",
                    "                'S' : -2,",
                    "                'Se' : -2,",
                    "                'F' : -1,",
                    "                'Cl' : -1,",
                    "                'Br' : -1,",
                    "                'I' : -1,",
                    "                'Fo' : -1}",
                    "    ",
                    "    @property",
                    "    def plus_one(self):",
                    "        \"\"\"",
                    "        returns list of elements (str) likely to be 1+",
                    "        \"\"\"",
                    "        return ['H', 'Li', 'Na', 'K', 'Rb', 'Cs', 'Fr', 'Ag']",
                    "    ",
                    "    @property",
                    "    def plus_two(self):",
                    "        \"\"\"",
                    "        returns list of elements (str) likely to be 2+",
                    "        \"\"\"        ",
                    "        return ['Be', 'Mg', 'Ca', 'Sr', 'Ba', 'Ra']",
                    "    ",
                    "    @property",
                    "    def plus_three(self):",
                    "        \"\"\"",
                    "        returns list of elements (str) likely to be 3+",
                    "        \"\"\"        ",
                    "        return ['Sc', 'Y', 'La', 'Al', 'Ga', 'In',",
                    "                'Pr', 'Nd', 'Pm', 'Sm', 'Eu', 'Gd', 'Tb',",
                    "                'Dy', 'Ho', 'Er', 'Tm', 'Yb', 'Lu']    ",
                    "    ",
                    "    @property",
                    "    def cations(self):",
                    "        \"\"\"",
                    "        returns list of cations (str)",
                    "        \"\"\"",
                    "        return self.As + self.Bs",
                    "    ",
                    "    @property",
                    "    def anions(self):",
                    "        \"\"\"",
                    "        returns list of anions (str)",
                    "        \"\"\"        ",
                    "        return self.Xs",
                    "    ",
                    "    @property",
                    "    def chi_dict(self):",
                    "        \"\"\"",
                    "        returns {el (str) : Pauling electronegativity (float)} for cations",
                    "        \"\"\"",
                    "        cations = self.cations",
                    "        chi_dict = {}",
                    "        df1 = pd.read_csv(\"https://gitlab.mpcdf.mpg.de/nomad-lab/nomad-lab-base/raw/master/analysis-tools/perovsktie-predictor/electronegativities.csv\")",
                    "        tmp_cation = []",
                    "        tmp_cation.append(df1.columns.tolist()[0])",
                    "        tmp_cation.extend(df1[df1.columns.tolist()[0]])",
                    "        tmp_el = []",
                    "        tmp_el.append( df1.columns.tolist()[1] )",
                    "        tmp_el.extend( df1[df1.columns.tolist()[1]] )",
                    "        df2 = pd.DataFrame({\"cation\":tmp_cation, ",
                    "                     \"el\":tmp_el})",
                    "        # generate a dictionary of {cation : electronegativity} for help with assignment",
                    "        for cation in cations:",
                    "            idx = df2.index[df2['cation'] == cation].tolist()",
                    "            chi_dict[df2.loc[idx].cation.values[0]] = float(df2.loc[idx].el.values[0])",
                    "        return chi_dict",
                    "",
                    "    @property",
                    "    def site_dict(self):",
                    "        \"\"\"",
                    "        returns dictionary of {el : [el_SITE0, el_SITE1, ...]}",
                    "        \"\"\"",
                    "        els = self.atom_names",
                    "        nums = self.atom_nums",
                    "        site_dict = {els[idx] : ['_'.join([els[idx], str(counter)]) for counter in range(nums[idx])] for idx in range(len(els))}",
                    "        return site_dict",
                    "  ",
                    "    @property",
                    "    def allowed_ox(self):",
                    "        \"\"\"",
                    "        returns {el (str) : list of allowed oxidation states (int)} for cations",
                    "        \"\"\"",
                    "        site_dict = self.site_dict",
                    "        Xs = self.Xs",
                    "        cations = self.cations",
                    "        ox_dict = {}",
                    "        for cation in cations:",
                    "            tmp_dict1 = {}",
                    "            sites = site_dict[cation]",
                    "            for site in sites:",
                    "                tmp_dict2 = {}",
                    "                if cation in self.plus_one:",
                    "                    oxs = [1]",
                    "                elif cation in self.plus_two:",
                    "                    oxs = [2]",
                    "                else:",
                    "                    oxs = [val for val in list(Shannon_dict[cation].keys()) if val > 0]",
                    "                tmp_dict2['oxs'] = oxs",
                    "                tmp_dict1[site] = tmp_dict2",
                    "            ox_dict[cation] = tmp_dict1",
                    "        for X in Xs:",
                    "            tmp_dict1 = {}",
                    "            sites = site_dict[X]",
                    "            for site in sites:",
                    "                tmp_dict2 = {}",
                    "                tmp_dict2['oxs'] = [self.X_ox_dict[X]]",
                    "                tmp_dict1[site] = tmp_dict2",
                    "            ox_dict[X] = tmp_dict1",
                    "        return ox_dict",
                    "    ",
                    "    @property",
                    "    def X_charge(self):",
                    "        \"\"\"",
                    "        returns the total charge of anions (float)",
                    "        \"\"\"",
                    "        charge = 0",
                    "        allowed_ox = self.allowed_ox",
                    "        for key in allowed_ox:",
                    "            if key in self.Xs:",
                    "                X_sites = allowed_ox[key]",
                    "                for X_site in X_sites:",
                    "                    charge += allowed_ox[key][X_site]['oxs'][0]",
                    "        return charge",
                    "    ",
                    "    @property",
                    "    def idx_dict(self):",
                    "        \"\"\"",
                    "        returns dictionary of {el : [idx0, idx1, ...]}",
                    "        \"\"\"",
                    "        cations = self.cations",
                    "        allowed_ox = self.allowed_ox",
                    "        idx_dict = {}",
                    "        count = 0",
                    "        for key in cations:",
                    "            num_sites = len(allowed_ox[key].keys())",
                    "            indices = list(np.arange(count, count + num_sites))",
                    "            count += num_sites",
                    "            idx_dict[key] = indices",
                    "        return idx_dict",
                    "    ",
                    "    @property",
                    "    def bal_combos(self):",
                    "        \"\"\"",
                    "        returns dictionary of {ox state combo (tup) : {el : [ox state by site (float)]}}",
                    "        \"\"\"",
                    "        X_charge = self.X_charge",
                    "        allowed_ox = self.allowed_ox",
                    "        idx_dict = self.idx_dict",
                    "        cations = self.cations",
                    "        lists = [allowed_ox[key][site]['oxs'] for key in cations for site in list(allowed_ox[key].keys())]",
                    "        combos = list(product(*lists))",
                    "        isovalent_combos = []",
                    "        suitable_combos = []",
                    "        for combo in combos:",
                    "            iso_count = 0",
                    "            suit_count = 0",
                    "            for key in idx_dict:",
                    "                curr_oxs = [combo[idx] for idx in idx_dict[key]]",
                    "                if np.min(curr_oxs) == np.max(curr_oxs):",
                    "                    iso_count += 1",
                    "                if np.min(curr_oxs) >= np.max(curr_oxs) - 1:",
                    "                    suit_count += 1",
                    "            if iso_count == len(cations):",
                    "                isovalent_combos.append(combo)",
                    "            if suit_count == len(cations):",
                    "                suitable_combos.append(combo)",
                    "        bal_combos = [combo for combo in isovalent_combos if np.sum(combo) == -X_charge]",
                    "        if len(bal_combos) > 0:",
                    "            combo_to_idx_ox = {}",
                    "            for combo in bal_combos:",
                    "                idx_to_ox = {}",
                    "                for key in idx_dict:",
                    "                    idx_to_ox[key] = sorted([combo[idx] for idx in idx_dict[key]])",
                    "                    if idx_to_ox not in list(combo_to_idx_ox.values()):",
                    "                        combo_to_idx_ox[combo] = idx_to_ox                    ",
                    "                combo_to_idx_ox[combo] = idx_to_ox",
                    "            return combo_to_idx_ox",
                    "        else:",
                    "            bal_combos = [combo for combo in suitable_combos if combo not in isovalent_combos if np.sum(combo) == -X_charge]",
                    "            combo_to_idx_ox = {}",
                    "            for combo in bal_combos:",
                    "                idx_to_ox = {}",
                    "                for key in idx_dict:",
                    "                    idx_to_ox[key] = sorted([combo[idx] for idx in idx_dict[key]])",
                    "                    if idx_to_ox not in list(combo_to_idx_ox.values()):",
                    "                        combo_to_idx_ox[combo] = idx_to_ox",
                    "            return combo_to_idx_ox",
                    "        ",
                    "    @property",
                    "    def unique_combos(self):",
                    "        \"\"\"",
                    "        returns unique version of self.bal_combos",
                    "        \"\"\"",
                    "        combos = self.bal_combos",
                    "        if isinstance(combos, float) or len(combos) == 0:",
                    "            return np.nan",
                    "        unique_combos = {}",
                    "        for combo in combos:",
                    "            if combos[combo] not in list(unique_combos.values()):",
                    "                unique_combos[combo] = combos[combo]",
                    "        return unique_combos",
                    "        ",
                    "        ",
                    "    @property",
                    "    def combos_near_isovalency(self):",
                    "        \"\"\"",
                    "        returns dictionary of most isovalent (within element) unique combos",
                    "        \"\"\"",
                    "        combos = self.unique_combos",
                    "        if isinstance(combos, float) or len(combos) == 0:",
                    "            return np.nan        ",
                    "        cations = self.cations",
                    "        hetero_dict = {}",
                    "        for combo in combos:",
                    "            sum_states = 0",
                    "            for cation in cations:",
                    "                sum_states += len(list(set(combos[combo][cation])))",
                    "            hetero_dict[combo] = sum_states - len(set(cations))",
                    "        min_heterovalency = np.min(list(hetero_dict.values()))",
                    "        near_iso_dict = {}",
                    "        for combo in combos:",
                    "            if hetero_dict[combo] == min_heterovalency:",
                    "                near_iso_dict[combo] = combos[combo]",
                    "        return near_iso_dict",
                    "    ",
                    "    @property",
                    "    def choice_dict(self):",
                    "        \"\"\"",
                    "        returns dictionary of {el (str) : [potential ox states (float)]}",
                    "        \"\"\"",
                    "        combos = self.combos_near_isovalency",
                    "        if isinstance(combos, float) or len(combos) == 0:",
                    "            return np.nan        ",
                    "        cations = self.cations",
                    "        choices = {cation : [] for cation in cations}",
                    "        for cation in cations:",
                    "            for combo in combos:",
                    "                choices[cation].extend(combos[combo][cation])",
                    "                choices[cation] = list(set(choices[cation]))",
                    "        return choices",
                    "    ",
                    "    @property",
                    "    def chosen_ox_states(self):",
                    "        \"\"\"",
                    "        returns dictionary of {el (str) : chosen ox state (float)}",
                    "        \"\"\"",
                    "        cations = self.cations",
                    "        conc_dict = self.conc_dict",
                    "        els = self.els",
                    "        choices = self.choice_dict",
                    "        if isinstance(choices, float):",
                    "            return np.nan        ",
                    "        X_ox_dict = self.X_ox_dict",
                    "        ox_dict = {}",
                    "        ox_dict[self.X1] = X_ox_dict[self.X1]",
                    "        if self.X1 != self.X2:",
                    "            ox_dict[self.X2] = X_ox_dict[self.X2]",
                    "        for cation in cations:",
                    "            if len(choices[cation]) == 1:",
                    "                ox_dict[cation] = choices[cation][0]",
                    "        if len(ox_dict) == len(els):",
                    "            return ox_dict",
                    "        else:",
                    "            unspec_els = [el for el in els if el not in ox_dict]",
                    "            unspec_charge = -np.sum([conc_dict[el]*ox_dict[el] for el in ox_dict])",
                    "            if len(unspec_els) == 1:",
                    "                unspec_combos = list(product(choices[unspec_els[0]]))",
                    "            elif len(unspec_els) == 2:",
                    "                unspec_combos = list(product(choices[unspec_els[0]], choices[unspec_els[1]]))",
                    "            elif len(unspec_els) == 3:",
                    "                unspec_combos = list(product(choices[unspec_els[0]], choices[unspec_els[1]], choices[unspec_els[2]]))",
                    "            elif len(unspec_els) == 4:",
                    "                unspec_combos = list(product(choices[unspec_els[0]], choices[unspec_els[1]], choices[unspec_els[2]], choices[unspec_els[3]]))",
                    "            elif len(unspec_els) == 5:",
                    "                unspec_combos = list(product(choices[unspec_els[0]], choices[unspec_els[1]], choices[unspec_els[2]], choices[unspec_els[3]], choices[unspec_els[4]]))  ",
                    "            elif len(unspec_els) == 6:",
                    "                unspec_combos = list(product(choices[unspec_els[0]], choices[unspec_els[1]], choices[unspec_els[2]], choices[unspec_els[3]], choices[unspec_els[4]], choices[unspec_els[5]]))                 ",
                    "            good_combos = []",
                    "            for combo in unspec_combos:",
                    "                amt = 0",
                    "                for idx in range(len(unspec_els)):",
                    "                    amt += conc_dict[unspec_els[idx]]*combo[idx]",
                    "                if amt == unspec_charge:",
                    "                    good_combos.append(combo) ",
                    "            if len(good_combos) == 0:",
                    "                return np.nan",
                    "            biggest_spread = np.max([np.max(combo) - np.min(combo) for combo in good_combos])",
                    "            smallest_spread = np.min([np.max(combo) - np.min(combo) for combo in good_combos])",
                    "            spread_combos = [combo for combo in good_combos if np.max(combo) - np.min(combo) == biggest_spread]",
                    "            tight_combos = [combo for combo in good_combos if np.max(combo) - np.min(combo) == smallest_spread]",
                    "            chi_dict = self.chi_dict",
                    "            chis = [chi_dict[el] for el in unspec_els]",
                    "            maxdex = chis.index(np.max(chis))",
                    "            mindex = chis.index(np.min(chis))",
                    "            if np.min(chis) <= 0.9*np.max(chis):",
                    "                if len(spread_combos) > 1:",
                    "                    for combo in spread_combos:",
                    "                        if combo[mindex] == np.max(combo):",
                    "                            if combo[maxdex] == np.min(combo):",
                    "                                for idx in range(len(unspec_els)):",
                    "                                    el = unspec_els[idx]",
                    "                                    ox_dict[el] = combo[idx]",
                    "                                return ox_dict",
                    "                    min_ox_most_elec = np.min([combo[maxdex] for combo in spread_combos])",
                    "                    for combo in spread_combos:",
                    "                        if (combo[maxdex] == min_ox_most_elec):",
                    "                            for idx in range(len(unspec_els)):",
                    "                                el = unspec_els[idx]",
                    "                                ox_dict[el] = combo[idx]",
                    "                            return ox_dict",
                    "                else:",
                    "                    combo = spread_combos[0]",
                    "                    for idx in range(len(unspec_els)):",
                    "                        el = unspec_els[idx]",
                    "                        ox_dict[el] = combo[idx]",
                    "            else:",
                    "                if len(tight_combos) > 1:",
                    "                    for combo in tight_combos:",
                    "                        if combo[mindex] == np.max(combo):",
                    "                            if combo[maxdex] == np.min(combo):",
                    "                                for idx in range(len(unspec_els)):",
                    "                                    el = unspec_els[idx]",
                    "                                    ox_dict[el] = combo[idx]",
                    "                else:",
                    "                    combo = tight_combos[0]",
                    "                    for idx in range(len(unspec_els)):",
                    "                        el = unspec_els[idx]",
                    "                        ox_dict[el] = combo[idx]",
                    "        return ox_dict",
                    "    ",
                    "    @property",
                    "    def AB_radii_dict(self):",
                    "        \"\"\"",
                    "        returns {el (str) : {'A_rad' : radius if A (float),",
                    "                             'B_rad' : radius if B (float)}}",
                    "        \"\"\"",
                    "        ox_dict = self.chosen_ox_states",
                    "        if isinstance(ox_dict, float):",
                    "            return np.nan",
                    "        radii_dict = {}",
                    "        for el in ox_dict:",
                    "            if el not in [self.X1, self.X2]:",
                    "                tmp_dict = {}",
                    "                ox = ox_dict[el]",
                    "                coords = list(Shannon_dict[el][ox].keys())",
                    "                B_coords = [abs(coord - 6) for coord in coords]",
                    "                mindex = [idx for idx in range(len(B_coords)) if B_coords[idx] == np.min(B_coords)][0]",
                    "                B_coord = coords[mindex]",
                    "                A_coords = [abs(coord - 12) for coord in coords]",
                    "                mindex = [idx for idx in range(len(A_coords)) if A_coords[idx] == np.min(A_coords)][0]",
                    "                A_coord = coords[mindex]",
                    "                B_rad = Shannon_dict[el][ox][B_coord]['only_spin']",
                    "                A_rad = Shannon_dict[el][ox][A_coord]['only_spin']",
                    "                tmp_dict['A_rad'] = A_rad",
                    "                tmp_dict['B_rad'] = B_rad",
                    "                radii_dict[el] = tmp_dict",
                    "        return radii_dict",
                    "    ",
                    "    @property",
                    "    def nA1(self):",
                    "        \"\"\"",
                    "        returns oxidation state assigned to A (float)",
                    "        \"\"\"",
                    "        if self.is_single == 1:",
                    "            CCX3 = ''.join(self.As + self.Bs + self.Xs + ['3'])",
                    "            return PredictABX3(CCX3).nA        ",
                    "        if isinstance(self.chosen_ox_states, float):",
                    "            return np.nan",
                    "        else:",
                    "            return self.chosen_ox_states[self.A1]",
                    "        ",
                    "    @property",
                    "    def nA2(self):",
                    "        \"\"\"",
                    "        returns oxidation state assigned to A (float)",
                    "        \"\"\"",
                    "        if self.is_single == 1:",
                    "            CCX3 = ''.join(self.As + self.Bs + self.Xs + ['3'])",
                    "            return PredictABX3(CCX3).nA        ",
                    "        if isinstance(self.chosen_ox_states, float):",
                    "            return np.nan",
                    "        else:",
                    "            return self.chosen_ox_states[self.A2]    ",
                    "        ",
                    "    @property",
                    "    def nA(self):",
                    "        \"\"\"",
                    "        returns predicted Shannon ionic radius for B (float)",
                    "        \"\"\"",
                    "        if self.is_single == 1:",
                    "            CCX3 = ''.join(self.As + self.Bs + self.Xs + ['3'])",
                    "            return PredictABX3(CCX3).nA",
                    "        if isinstance(self.AB_radii_dict, float):",
                    "            return np.nan",
                    "        else:      ",
                    "            return np.mean([self.nA1, self.nA2])    ",
                    "    ",
                    "    @property",
                    "    def nB1(self):",
                    "        \"\"\"",
                    "        returns oxidation state assigned to A (int)",
                    "        \"\"\"",
                    "        if self.is_single == 1:",
                    "            CCX3 = ''.join(self.As + self.Bs + self.Xs + ['3'])",
                    "            return PredictABX3(CCX3).nB          ",
                    "        if isinstance(self.chosen_ox_states, float):",
                    "            return np.nan",
                    "        else:",
                    "            return self.chosen_ox_states[self.B1]",
                    "        ",
                    "    @property",
                    "    def nB2(self):",
                    "        \"\"\"",
                    "        returns oxidation state assigned to A (int)",
                    "        \"\"\"",
                    "        if self.is_single == 1:",
                    "            CCX3 = ''.join(self.As + self.Bs + self.Xs + ['3'])",
                    "            return PredictABX3(CCX3).nB        ",
                    "        if isinstance(self.chosen_ox_states, float):",
                    "            return np.nan",
                    "        else:",
                    "            return self.chosen_ox_states[self.B2]",
                    "        ",
                    "    @property",
                    "    def nB(self):",
                    "        \"\"\"",
                    "        returns predicted Shannon ionic radius for B (float)",
                    "        \"\"\"",
                    "        if self.is_single == 1:",
                    "            CCX3 = ''.join(self.As + self.Bs + self.Xs + ['3'])",
                    "            return PredictABX3(CCX3).nB        ",
                    "        if isinstance(self.AB_radii_dict, float):",
                    "            return np.nan",
                    "        else:      ",
                    "            return np.mean([self.nB1, self.nB2])",
                    "        ",
                    "    @property",
                    "    def nX1(self):",
                    "        \"\"\"",
                    "        returns oxidation state assigned to X1 (int)",
                    "        \"\"\"",
                    "        if self.is_single == 1:",
                    "            CCX3 = ''.join(self.As + self.Bs + self.Xs + ['3'])",
                    "            return PredictABX3(CCX3).nX        ",
                    "        if isinstance(self.chosen_ox_states, float):",
                    "            return np.nan",
                    "        else:",
                    "            return self.X_ox_dict[self.X1]    ",
                    "        ",
                    "    @property",
                    "    def nX2(self):",
                    "        \"\"\"",
                    "        returns oxidation state assigned to X2 (int)",
                    "        \"\"\"",
                    "        if self.is_single == 1:",
                    "            CCX3 = ''.join(self.As + self.Bs + self.Xs + ['3'])",
                    "            return PredictABX3(CCX3).nX        ",
                    "        if isinstance(self.chosen_ox_states, float):",
                    "            return np.nan",
                    "        else:",
                    "            return self.X_ox_dict[self.X2]          ",
                    "",
                    "    @property",
                    "    def nX(self):",
                    "        \"\"\"",
                    "        returns predicted Shannon ionic radius for X (float)",
                    "        \"\"\"",
                    "        if self.is_single == 1:",
                    "            CCX3 = ''.join(self.As + self.Bs + self.Xs + ['3'])",
                    "            return PredictABX3(CCX3).nX       ",
                    "        if isinstance(self.AB_radii_dict, float):",
                    "            return np.nan",
                    "        else:      ",
                    "            return np.mean([self.nX1, self.nX2])            ",
                    "    ",
                    "    @property",
                    "    def rA1(self):",
                    "        \"\"\"",
                    "        returns predicted Shannon ionic radius for A (float)",
                    "        \"\"\"",
                    "        if self.is_single == 1:",
                    "            CCX3 = ''.join(self.As + self.Bs + self.Xs + ['3'])",
                    "            return PredictABX3(CCX3).rA             ",
                    "        if isinstance(self.AB_radii_dict, float):",
                    "            return np.nan",
                    "        else:        ",
                    "            return self.AB_radii_dict[self.A1]['A_rad']",
                    "        ",
                    "    @property",
                    "    def rA2(self):",
                    "        \"\"\"",
                    "        returns predicted Shannon ionic radius for A (float)",
                    "        \"\"\"",
                    "        if self.is_single == 1:",
                    "            CCX3 = ''.join(self.As + self.Bs + self.Xs + ['3'])",
                    "            return PredictABX3(CCX3).rA             ",
                    "        if isinstance(self.AB_radii_dict, float):",
                    "            return np.nan",
                    "        else:        ",
                    "            return self.AB_radii_dict[self.A2]['A_rad']",
                    "",
                    "    @property",
                    "    def rA(self):",
                    "        \"\"\"",
                    "        returns predicted Shannon ionic radius for B (float)",
                    "        \"\"\"",
                    "        if self.is_single == 1:",
                    "            CCX3 = ''.join(self.As + self.Bs + self.Xs + ['3'])",
                    "            return PredictABX3(CCX3).rA        ",
                    "        if isinstance(self.AB_radii_dict, float):",
                    "            return np.nan",
                    "        else:      ",
                    "            return np.mean([self.rA1, self.rA2])        ",
                    "        ",
                    "    @property",
                    "    def rB1(self):",
                    "        \"\"\"",
                    "        returns predicted Shannon ionic radius for B (float)",
                    "        \"\"\"",
                    "        if self.is_single == 1:",
                    "            CCX3 = ''.join(self.As + self.Bs + self.Xs + ['3'])",
                    "            return PredictABX3(CCX3).rB           ",
                    "        if isinstance(self.AB_radii_dict, float):",
                    "            return np.nan",
                    "        else:        ",
                    "            return self.AB_radii_dict[self.B1]['B_rad']",
                    "        ",
                    "    @property",
                    "    def rB2(self):",
                    "        \"\"\"",
                    "        returns predicted Shannon ionic radius for B' (float)",
                    "        \"\"\"",
                    "        if self.is_single == 1:",
                    "            CCX3 = ''.join(self.As + self.Bs + self.Xs + ['3'])",
                    "            return PredictABX3(CCX3).rB           ",
                    "        if isinstance(self.AB_radii_dict, float):",
                    "            return np.nan",
                    "        else:        ",
                    "            return self.AB_radii_dict[self.B2]['B_rad']       ",
                    "    ",
                    "    @property",
                    "    def rB(self):",
                    "        \"\"\"",
                    "        returns predicted Shannon ionic radius for B (float)",
                    "        \"\"\"",
                    "        if self.is_single == 1:",
                    "            CCX3 = ''.join(self.As + self.Bs + self.Xs + ['3'])",
                    "            return PredictABX3(CCX3).rB        ",
                    "        if isinstance(self.AB_radii_dict, float):",
                    "            return np.nan",
                    "        else:      ",
                    "            return np.mean([self.rB1, self.rB2])",
                    "    ",
                    "    @property",
                    "    def rX1(self):",
                    "        \"\"\"",
                    "        returns Shannon ionic radius for X (float)",
                    "        \"\"\"",
                    "        if self.is_single == 1:",
                    "            CCX3 = ''.join(self.As + self.Bs + self.Xs + ['3'])",
                    "            return PredictABX3(CCX3).rX         ",
                    "        if self.X1 != 'N':",
                    "            return Shannon_dict[self.X1][self.X_ox_dict[self.X1]][6]['only_spin']",
                    "        else:",
                    "            return Shannon_dict[self.X1][self.X_ox_dict[self.X1]][4]['only_spin']",
                    "    ",
                    "    @property",
                    "    def rX2(self):",
                    "        \"\"\"",
                    "        returns Shannon ionic radius for X' (float)",
                    "        \"\"\"",
                    "        if self.is_single == 1:",
                    "            CCX3 = ''.join(self.As + self.Bs + self.Xs + ['3'])",
                    "            return PredictABX3(CCX3).rX          ",
                    "        if self.X2 != 'N':",
                    "            return Shannon_dict[self.X2][self.X_ox_dict[self.X2]][6]['only_spin']",
                    "        else:",
                    "            return Shannon_dict[self.X2][self.X_ox_dict[self.X2]][4]['only_spin']",
                    "    ",
                    "    @property",
                    "    def rX(self):",
                    "        \"\"\"",
                    "        returns predicted Shannon ionic radius for X (float)",
                    "        \"\"\"",
                    "        if self.is_single == 1:",
                    "            CCX3 = ''.join(self.As + self.Bs + self.Xs + ['3'])",
                    "            return PredictABX3(CCX3).rX        ",
                    "        if isinstance(self.AB_radii_dict, float):",
                    "            return np.nan",
                    "        else:",
                    "            return np.mean([self.rX1, self.rX2])    ",
                    "    ",
                    "    @property",
                    "    def mu(self):",
                    "        \"\"\"",
                    "        returns the predicted octahedral factor (float)",
                    "        \"\"\"",
                    "        if self.is_single == 1:",
                    "            CCX3 = ''.join(self.As + self.Bs + self.Xs + ['3'])",
                    "            return PredictABX3(CCX3).mu      ",
                    "        if isinstance(self.AB_radii_dict, float):",
                    "            return np.nan",
                    "        else:          ",
                    "            return self.rB / self.rX",
                    "    ",
                    "    @property",
                    "    def t(self):",
                    "        \"\"\"",
                    "        returns the predicted Goldschmidt tolerance factor (float)",
                    "        \"\"\"",
                    "        if self.is_single == 1:",
                    "            CCX3 = ''.join(self.As + self.Bs + self.Xs + ['3'])",
                    "            return PredictABX3(CCX3).t        ",
                    "        if isinstance(self.AB_radii_dict, float):",
                    "            return np.nan",
                    "        else:          ",
                    "            return (self.rA + self.rX) / (np.sqrt(2) * (self.rB + self.rX))",
                    "        ",
                    "    @property",
                    "    def t_pred(self):",
                    "        \"\"\"",
                    "        returns 1 if perovskite or -1 if nonperovskite by t",
                    "        \"\"\"",
                    "        if self.is_single == 1:",
                    "            CCX3 = ''.join(self.As + self.Bs + self.Xs + ['3'])",
                    "            return PredictABX3(CCX3).t_pred        ",
                    "        if math.isnan(self.t):",
                    "            return np.nan",
                    "        else:",
                    "            return [1 if (self.t > 0.825) and (self.t < 1.059) else -1][0]",
                    "        ",
                    "    @property",
                    "    def tau(self):",
                    "        \"\"\"",
                    "        returns tau",
                    "        \"\"\"",
                    "        if self.is_single == 1:",
                    "            CCX3 = ''.join(self.As + self.Bs + self.Xs + ['3'])",
                    "            return PredictABX3(CCX3).tau      ",
                    "        if isinstance(self.AB_radii_dict, float):",
                    "            return np.nan",
                    "        else:",
                    "            if self.rA <= self.rB:",
                    "                return np.nan",
                    "            else:",
                    "                return ((1/self.mu) - (self.nA)**2 + (self.nA) * (self.rA/self.rB)/(np.log(self.rA/self.rB)))",
                    "            ",
                    "    @property",
                    "    def tau_pred(self):",
                    "        \"\"\"",
                    "        returns 1 if perovskite or -1 if nonperovskite by tau",
                    "        \"\"\"",
                    "        if self.is_single == 1:",
                    "            CCX3 = ''.join(self.As + self.Bs + self.Xs + ['3'])",
                    "            return PredictABX3(CCX3).tau_pred        ",
                    "        if math.isnan(self.tau):",
                    "            return np.nan",
                    "        else:",
                    "            return [1 if self.tau < 4.18 else -1][0]",
                    "        ",
                    "    @property",
                    "    def calibrate_tau(self):",
                    "        path_to_save = \"https://gitlab.mpcdf.mpg.de/nomad-lab/nomad-lab-base/raw/master/analysis-tools/perovsktie-predictor/\"",
                    "        if not os.path.exists(os.path.join(path_to_save, 'save_clf12345.p')):",
                    "            print('creating classifier')",
                    "            np.random.seed(123)                  ",
                    "            df = pd.read_csv(os.path.join(path_to_save, 'TableS1.csv'))",
                    "            #df = pd.read_csv('TableS1.csv')",
                    "            #df = df.sample(n=20)",
                    "            df['tau'] = [PredictABX3(ABX3).tau for ABX3 in df.ABX3.values]",
                    "            X, y = df['tau'].values.reshape(-1, 1), df['exp_label'].values",
                    "            clf = CalibratedClassifierCV(cv=3)",
                    "            clf.fit(X, y)",
                    "            pickle.dump( clf, open(os.path.join(path_to_save, 'save_clf.p'), \"wb\" )  )",
                    "            return clf",
                    "        else:",
                    "            return pickle.load(open(os.path.join(path_to_save, 'save_clf.p'), \"rb\" ))",
                    "                                  ",
                    "        \"\"\"",
                    "        returns a calibrated classifier to yield tau probabilities",
                    "        \"\"\"",
                    "",
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                    "    ",
                    "#    @property",
                    "#    def calibrate_tau(self):",
                    "#        \"\"\"",
                    "#        returns a calibrated classifier to yield tau probabilities",
                    "#        \"\"\"",
                    "#        df = pd.read_csv('TableS1.csv')",
                    "#        df['tau'] = [PredictABX3(ABX3).tau for ABX3 in df.ABX3.values]",
                    "#        X, y = df['tau'].values.reshape(-1, 1), df['exp_label'].values",
                    "#        clf = CalibratedClassifierCV(cv=3)",
                    "#        clf.fit(X, y)",
                    "#        return clf",
                    "    ",
                    "    def tau_prob(self, clf):",
                    "        \"\"\"",
                    "        Args:",
                    "            clf (sklearn object) - calibrated classifier based on tau",
                    "        Returns:",
                    "            probability of perovskite based on tau (float)",
                    "        \"\"\"",
                    "        if math.isnan(self.tau):",
                    "            return np.nan        ",
                    "        if self.is_single == 1:",
                    "            CCX3 = ''.join(self.As + self.Bs + self.Xs + ['3'])",
                    "            return PredictABX3(CCX3).tau_prob(clf) ",
                    "        else:",
                    "            X = [[self.tau]]",
                    "            return clf.predict_proba(X)[0][1]    ",
                    "        ",
                    "        ",
                    "#clf = PredictABX3('CaTiO3').calibrate_tau",
                    "#prob = PredictABX3(\"CaTiO3\").tau_prob(clf)",
                    "",
                    "#print(prob)",
                    ""
                ],
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            },
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                "selectedType": "Hidden",
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                "shellId": "323B25B8FC19425D87DCCFD0DA19E4B8",
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            "evaluatorReader": true,
            "lineCount": 1365,
            "tags": ""
        },
        {
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            "type": "code",
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                    "<style type=\"text/css\">",
                    "/*!",
                    " * Nomad Beaker Notebook Template",
                    " *",
                    " * @copyright  Copyright 2017 Fritz Haber Institute of the Max Planck Society,",
                    " *             Benjamin Regler - Apache 2.0 License",
                    " * @license    http://www.apache.org/licenses/LICENSE-2.0",
                    " * @author     Benjamin Regler",
                    " * @version    1.0.0",
                    " *",
                    " * Licensed under the Apache License, Version 2.0 (the \"License\");",
                    " * you may not use this file except in compliance with the License.",
                    " * You may obtain a copy of the License at",
                    " * ",
                    " *     http://www.apache.org/licenses/LICENSE-2.0",
                    " *",
                    " * Unless required by applicable law or agreed to in writing, software",
                    " * distributed under the License is distributed on an \"AS IS\" BASIS,",
                    " * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.",
                    " * See the License for the specific language governing permissions and",
                    " * limitations under the License.",
                    " */",
                    "p{margin-bottom:1.3em}h1,h2,h3,h4{margin:1.414em 0 .5em;font-weight:inherit;line-height:1.2}h1{margin-top:0;font-size:3.998em}h2{font-size:2.827em}h3{font-size:1.999em}h4{font-size:1.414em}.font_small,small{font-size:.707em}.notebook-container{font-size:16px}.notebook-container .bkr{font-size:100%;font-weight:400;line-height:1.45;color:#333}.nomad--header h2{color:#20335d;font-weight:700;margin:0 0 .2em}.nomad--header h3{color:#20335d;font-weight:700;margin-top:0;text-indent:-1em;padding-left:1em}.nomad--header h3:before{content:\"\\2014\";padding-right:.25em}.nomad--header .nomad--description{margin:-1em 0 0 2em}.atomic-data--block,.nomad--last-updated{display:inline-block;margin-top:1em}.nomad--last-updated{color:grey;float:right;position:relative;z-index:1}.nomad--last-updated::before{bottom:-75%;content:attr(data-version);font-size:4em;font-weight:700;opacity:.2;position:absolute;right:0}.atomic-data label{display:block;font-size:medium;font-weight:700}.atomic-data--select,.chosen-container{width:100%!important}.atomic-data--select:disabled{color:#d3d3d3}.atomic-data--reset-buton{display:inline-block;margin-top:1.6em;width:100%}.modal-dialog{max-width:1000px;width:80%}.modal-header h1{font-size:2em;line-height:1.2}.modal-dialog h2{font-size:1.414em}.modal-dialog h2:first-child{margin-top:0}.modal-dialog h3{font-size:1.2em}.modal-dialog dt{font-size:larger;margin-top:1.414em}.modal-dialog img{width:100%}.modal-dialog .authors{text-transform:uppercase}",
                    "</style>",
                    "",
                    "<div id=\"teaser\" style='background-color: rgba(149,170,79, 1.0); background-position:  right center; background-size: 200px; background-repeat: no-repeat; ",
                    "    padding-top: 20px;",
                    "    padding-right: 10px;",
                    "    padding-bottom: 50px;",
                    "    padding-left: 80px;' > ",
                    "",
                    "  <div class=\"nomad--header\">",
                    "   <div style=\"text-align:center\">",
                    "    <h2> <img id=\"nomad\" src=\"https://nomad-coe.eu/uploads/nomad/images/NOMAD_Logo2.png\" height=\"100\" alt=\"NOMAD Logo\">  NOMAD Analytics Toolkit   ",
                    "  <img id=\"nomad\" src=\"https://www.nomad-coe.eu/uploads/nomad/backgrounds/head_big-data_analytics_2.png\" height=\"80\" alt=\"NOMAD Logo\"> </h2>",
                    "  </div>",
                    "    <h3>Predicting the stability of perovskite oxides and halides using a new tolerance factor</h3>",
                    "    <p class=\"nomad--description\">",
                    "      created by:",
                    "      Christopher Bartel<sup>1</sup> (<a href=\"mailto:christopher.bartel@colorado.edu\">email</a>),",
                    "      Christopher Sutton<sup>2</sup> (<a href=\"mailto:sutton@fhi-berlin.mpg.de\">email</a>)",
                    "  <br><br>",
                    "   ",
                    "      <sup>1</sup> University of Colorado Boulder, 3415 Colorado Ave., Boulder, CO, USA <br>",
                    "      <sup>2</sup> Fritz Haber Institute of the Max Planck Society, Faradayweg 4-6, D-14195 Berlin, Germany <br>",
                    "      ",
                    "      <span class=\"nomad--last-updated\" data-version=\"v1.0.0\">[Last updated: October 16, 2017]</span>",
                    "    </p>",
                    "</div>",
                    "",
                    "</div>  ",
                    "",
                    "<div style='text-align: right;'>",
                    "<a href=\"https://analytics-toolkit.nomad-coe.eu/home/\" class=\"btn btn-primary\" style=\"font-size:larger;\">Back to Analytics Home</a> ",
                    "<a href=\"https://www.nomad-coe.eu/\" class=\"btn btn-primary\" style=\"font-size:larger;\">Back to nomad-coe</a> ",
                    "</div>",
                    ""
                ],
                "hidden": true
            },
            "output": {
                "state": {},
                "result": {
                    "type": "BeakerDisplay",
                    "innertype": "Html",
                    "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<style type=\"text/css\">\n/*!\n * Nomad Beaker Notebook Template\n *\n * @copyright  Copyright 2017 Fritz Haber Institute of the Max Planck Society,\n *             Benjamin Regler - Apache 2.0 License\n * @license    http://www.apache.org/licenses/LICENSE-2.0\n * @author     Benjamin Regler\n * @version    1.0.0\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n * \n *     http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\np{margin-bottom:1.3em}h1,h2,h3,h4{margin:1.414em 0 .5em;font-weight:inherit;line-height:1.2}h1{margin-top:0;font-size:3.998em}h2{font-size:2.827em}h3{font-size:1.999em}h4{font-size:1.414em}.font_small,small{font-size:.707em}.notebook-container{font-size:16px}.notebook-container .bkr{font-size:100%;font-weight:400;line-height:1.45;color:#333}.nomad--header h2{color:#20335d;font-weight:700;margin:0 0 .2em}.nomad--header h3{color:#20335d;font-weight:700;margin-top:0;text-indent:-1em;padding-left:1em}.nomad--header h3:before{content:\"\\2014\";padding-right:.25em}.nomad--header .nomad--description{margin:-1em 0 0 2em}.atomic-data--block,.nomad--last-updated{display:inline-block;margin-top:1em}.nomad--last-updated{color:grey;float:right;position:relative;z-index:1}.nomad--last-updated::before{bottom:-75%;content:attr(data-version);font-size:4em;font-weight:700;opacity:.2;position:absolute;right:0}.atomic-data label{display:block;font-size:medium;font-weight:700}.atomic-data--select,.chosen-container{width:100%!important}.atomic-data--select:disabled{color:#d3d3d3}.atomic-data--reset-buton{display:inline-block;margin-top:1.6em;width:100%}.modal-dialog{max-width:1000px;width:80%}.modal-header h1{font-size:2em;line-height:1.2}.modal-dialog h2{font-size:1.414em}.modal-dialog h2:first-child{margin-top:0}.modal-dialog h3{font-size:1.2em}.modal-dialog dt{font-size:larger;margin-top:1.414em}.modal-dialog img{width:100%}.modal-dialog .authors{text-transform:uppercase}\n</style>\n\n<div id=\"teaser\" style=\"background-color: rgba(149,170,79, 1.0); background-position:  right center; background-size: 200px; background-repeat: no-repeat; \n    padding-top: 20px;\n    padding-right: 10px;\n    padding-bottom: 50px;\n    padding-left: 80px;\"> \n\n  <div class=\"nomad--header\">\n   <div style=\"text-align:center\">\n    <h2> <img id=\"nomad\" src=\"https://nomad-coe.eu/uploads/nomad/images/NOMAD_Logo2.png\" height=\"100\" alt=\"NOMAD Logo\">  NOMAD Analytics Toolkit   \n  <img id=\"nomad\" src=\"https://www.nomad-coe.eu/uploads/nomad/backgrounds/head_big-data_analytics_2.png\" height=\"80\" alt=\"NOMAD Logo\"> </h2>\n  </div>\n    <h3>Predicting the stability of perovskite oxides and halides using a new tolerance factor</h3>\n    <p class=\"nomad--description\">\n      created by:\n      Christopher Bartel<sup>1</sup> (<a href=\"mailto:christopher.bartel@colorado.edu\">email</a>),\n      Christopher Sutton<sup>2</sup> (<a href=\"mailto:sutton@fhi-berlin.mpg.de\">email</a>)\n  <br><br>\n   \n      <sup>1</sup> University of Colorado Boulder, 3415 Colorado Ave., Boulder, CO, USA <br>\n      <sup>2</sup> Fritz Haber Institute of the Max Planck Society, Faradayweg 4-6, D-14195 Berlin, Germany <br>\n      \n      <span class=\"nomad--last-updated\" data-version=\"v1.0.0\">[Last updated: October 16, 2017]</span>\n    </p>\n</div>\n\n</div>  \n\n<div style=\"text-align: right;\">\n<a href=\"https://analytics-toolkit.nomad-coe.eu/home/\" class=\"btn btn-primary\" style=\"font-size:larger;\">Back to Analytics Home</a> \n<a href=\"https://www.nomad-coe.eu/\" class=\"btn btn-primary\" style=\"font-size:larger;\">Back to nomad-coe</a> \n</div>\n"
                },
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                "height": 389
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            },
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        {
            "id": "codeokNLxg",
            "type": "code",
            "evaluator": "HTML",
            "input": {
                "body": [
                    "<legend>Descriptor for Perovskite Stability</legend>",
                    "",
                    "<img src=\"http://latex.codecogs.com/svg.latex?\\tau\" border=\"0\"/> is a descriptor that takes as input the chemical composition <img src=\"http://latex.codecogs.com/svg.latex?(A,B,X)\" border=\"0\"/> and outputs a prediction of perovskite stability according to the formula:",
                    "",
                    "<p> <img src=\"http://latex.codecogs.com/svg.latex?\\tau =\\frac{r_X}{r_B} - n_A(n_A - \\frac{\\frac{r_A}{r_B}}{ln(\\frac{r_A}{r_B})}),\" border=\"0\"/><br />",
                    "  ",
                    "  where <img src=\"http://latex.codecogs.com/svg.latex?r_i\" border=\"0\"/> is the ionic radius of ion, <img src=\"http://latex.codecogs.com/svg.latex?i\" border=\"0\"/> and <img src=\"http://latex.codecogs.com/svg.latex?n_i\" border=\"0\"/> is the oxidation state of ion, <img src=\"http://latex.codecogs.com/svg.latex?i\" border=\"0\"/>, and <img src=\"http://latex.codecogs.com/svg.latex?\\tau<4.18\" border=\"0\"/> indicates stability in the perovskite structure.<br />",
                    "  ",
                    "<p> This descriptor was identified by applying the SISSO algorithm developed by R. Ouyang, S. Curtarolo, E. Ahmetick, M. Scheffler, L. Ghiringhelli: Phys. Rev. Materials 2, 083802 (2018) [<a href=\"https://journals.aps.org/prmaterials/pdf/10.1103/PhysRevMaterials.2.083802\">PDF</a>] {<a href=\"https://github.com/rouyang2017/SISSO\">Code</a>} which efficiently identifies <img src=\"http://latex.codecogs.com/svg.latex?\\tau\" border=\"0\"/> from a space of ~3,000,000,000 potential descriptors. <br /><br />",
                    "  ",
                    "  <img src=\"http://latex.codecogs.com/svg.latex?\\tau\" border=\"0\"/> requires the same information as Goldschmidt's famous tolerance factor (noting that <img src=\"http://latex.codecogs.com/svg.latex?r\" border=\"0\"/> is an implict function of <img src=\"http://latex.codecogs.com/svg.latex?n\" border=\"0\"/>): <br /><img src=\"http://latex.codecogs.com/svg.latex?t =\\frac{r_A+r_X}{\\sqrt{2}(r_B+r_X)}.\" border=\"0\"/><br /><br /> ",
                    "  ",
                    "  While the functional forms are comparable, the accuracies are not.  On a set of 576 <img src=\"http://latex.codecogs.com/svg.latex?ABX_3\" border=\"0\"/> compounds characterized experimentally at ambient conditions, <img src=\"http://latex.codecogs.com/svg.latex?\\tau\" border=\"0\"/> achieves 92% accuracy in predicting whether the compound will or won't be stable as perovskite compared with 74% using <img src=\"http://latex.codecogs.com/svg.latex?t\" border=\"0\"/>. <br /><br />",
                    "  ",
                    "  <img src=\"http://latex.codecogs.com/svg.latex?\\tau\" border=\"0\"/> is probabilistic, providing not only whether a given composition will crystallize as perovskite but also a probability on this prediction. ",
                    "  ",
                    "  Below you can input two cations - <img src=\"http://latex.codecogs.com/svg.latex?A\" border=\"0\"/> and <img src=\"http://latex.codecogs.com/svg.latex?B\" border=\"0\"/> - and one anion - <img src=\"http://latex.codecogs.com/svg.latex?X\" border=\"0\"/> - and the utility will automatically assign oxidation states and radii to each ion (more on this below) and provide the probability that the <img src=\"http://latex.codecogs.com/svg.latex?ABX_3\" border=\"0\"/> will form the perovskite structure. <img src=\"http://latex.codecogs.com/svg.latex?t\" border=\"0\"/> is also provided for context.<br /><br />",
                    "  ",
                    "  This result is visualized with respect to the cationic radii to show where in the space of stable and unstable perovskites the given composition sits. <br /><br />",
                    "  ",
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                    "  The descriptor also generalizes to double perovskites - compounds with substitutions at the <img src=\"http://latex.codecogs.com/svg.latex?A\" border=\"0\"/>, <img src=\"http://latex.codecogs.com/svg.latex?B\" border=\"0\"/>, or <img src=\"http://latex.codecogs.com/svg.latex?X\" border=\"0\"/> sites. Below you can explore the stability of compounds with 50/50 mixtures of ions on each or all of the sites - i.e., <img src=\"http://latex.codecogs.com/svg.latex?(AA')(BB')(XX')_3\" border=\"0\"/> formulas. <br /><br />",
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                    "  ",
                    "  More details on the identification and application of <img src=\"http://latex.codecogs.com/svg.latex?\\tau\" border=\"0\"/> are available within the <a href=\"https://arxiv.org/abs/1801.07700\">manuscript</a> and associated <a href=\"https://github.com/CJBartel/perovskite-stability\">github repository</a>.",
                    "",
                    "</p>"
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                    "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<legend>Descriptor for Perovskite Stability</legend>\n\n<img src=\"http://latex.codecogs.com/svg.latex?\\tau\" border=\"0\"> is a descriptor that takes as input the chemical composition <img src=\"http://latex.codecogs.com/svg.latex?(A,B,X)\" border=\"0\"> and outputs a prediction of perovskite stability according to the formula:\n\n<p> <img src=\"http://latex.codecogs.com/svg.latex?\\tau =\\frac{r_X}{r_B} - n_A(n_A - \\frac{\\frac{r_A}{r_B}}{ln(\\frac{r_A}{r_B})}),\" border=\"0\"><br>\n  \n  where <img src=\"http://latex.codecogs.com/svg.latex?r_i\" border=\"0\"> is the ionic radius of ion, <img src=\"http://latex.codecogs.com/svg.latex?i\" border=\"0\"> and <img src=\"http://latex.codecogs.com/svg.latex?n_i\" border=\"0\"> is the oxidation state of ion, <img src=\"http://latex.codecogs.com/svg.latex?i\" border=\"0\">, and <img src=\"http://latex.codecogs.com/svg.latex?\\tau&lt;4.18\" border=\"0\"><!--4--> indicates stability in the perovskite structure.<br>\n  \n</p><p> This descriptor was identified by applying the SISSO algorithm developed by R. Ouyang, S. Curtarolo, E. Ahmetick, M. Scheffler, L. Ghiringhelli: Phys. Rev. Materials 2, 083802 (2018) [<a href=\"https://journals.aps.org/prmaterials/pdf/10.1103/PhysRevMaterials.2.083802\">PDF</a>] {<a href=\"https://github.com/rouyang2017/SISSO\">Code</a>} which efficiently identifies <img src=\"http://latex.codecogs.com/svg.latex?\\tau\" border=\"0\"> from a space of ~3,000,000,000 potential descriptors. <br><br>\n  \n  <img src=\"http://latex.codecogs.com/svg.latex?\\tau\" border=\"0\"> requires the same information as Goldschmidt's famous tolerance factor (noting that <img src=\"http://latex.codecogs.com/svg.latex?r\" border=\"0\"> is an implict function of <img src=\"http://latex.codecogs.com/svg.latex?n\" border=\"0\">): <br><img src=\"http://latex.codecogs.com/svg.latex?t =\\frac{r_A+r_X}{\\sqrt{2}(r_B+r_X)}.\" border=\"0\"><br><br> \n  \n  While the functional forms are comparable, the accuracies are not.  On a set of 576 <img src=\"http://latex.codecogs.com/svg.latex?ABX_3\" border=\"0\"> compounds characterized experimentally at ambient conditions, <img src=\"http://latex.codecogs.com/svg.latex?\\tau\" border=\"0\"> achieves 92% accuracy in predicting whether the compound will or won't be stable as perovskite compared with 74% using <img src=\"http://latex.codecogs.com/svg.latex?t\" border=\"0\">. <br><br>\n  \n  <img src=\"http://latex.codecogs.com/svg.latex?\\tau\" border=\"0\"> is probabilistic, providing not only whether a given composition will crystallize as perovskite but also a probability on this prediction. \n  \n  Below you can input two cations - <img src=\"http://latex.codecogs.com/svg.latex?A\" border=\"0\"> and <img src=\"http://latex.codecogs.com/svg.latex?B\" border=\"0\"> - and one anion - <img src=\"http://latex.codecogs.com/svg.latex?X\" border=\"0\"> - and the utility will automatically assign oxidation states and radii to each ion (more on this below) and provide the probability that the <img src=\"http://latex.codecogs.com/svg.latex?ABX_3\" border=\"0\"> will form the perovskite structure. <img src=\"http://latex.codecogs.com/svg.latex?t\" border=\"0\"> is also provided for context.<br><br>\n  \n  This result is visualized with respect to the cationic radii to show where in the space of stable and unstable perovskites the given composition sits. <br><br>\n  \n  The descriptor also generalizes to double perovskites - compounds with substitutions at the <img src=\"http://latex.codecogs.com/svg.latex?A\" border=\"0\">, <img src=\"http://latex.codecogs.com/svg.latex?B\" border=\"0\">, or <img src=\"http://latex.codecogs.com/svg.latex?X\" border=\"0\"> sites. Below you can explore the stability of compounds with 50/50 mixtures of ions on each or all of the sites - i.e., <img src=\"http://latex.codecogs.com/svg.latex?(AA')(BB')(XX')_3\" border=\"0\"> formulas. <br><br>\n  \n  More details on the identification and application of <img src=\"http://latex.codecogs.com/svg.latex?\\tau\" border=\"0\"> are available within the <a href=\"https://arxiv.org/abs/1801.07700\">manuscript</a> and associated <a href=\"https://github.com/CJBartel/perovskite-stability\">github repository</a>.\n\n</p>"
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                    "<legend>On assigning oxidation states and radii</legend>",
                    "",
                    "<img src=\"http://latex.codecogs.com/svg.latex?t\" border=\"0\"/> and <img src=\"http://latex.codecogs.com/svg.latex?\\tau\" border=\"0\"/> are both functions of the site-specific radii, <img src=\"http://latex.codecogs.com/svg.latex?r_i\" border=\"0\"/>, yet it is not known <i>a priori</i> for a new formula which cation will sit on the 12-fold coordinated <img src=\"http://latex.codecogs.com/svg.latex?A\" border=\"0\"/>-site and 6-fold coordinted <img src=\"http://latex.codecogs.com/svg.latex?B\" border=\"0\"/>-site. To address this, we developed a systematic approach for making this determination based on the condition that <img src=\"http://latex.codecogs.com/svg.latex?r_A > r_B\" border=\"0\"/> because of the larger coordination number in the perovskite structure.",
                    "",
                    "Ionic radii have been developed by a number of researchers in the last 100 years, but R.D. Shannon's set [<a href=\"http://scripts.iucr.org/cgi-bin/paper?S0567739476001551\">link</a>] are regarded as the most comprehensive, provided as a function of both oxidation state, <img src=\"http://latex.codecogs.com/svg.latex?n_i\", border=\"0\"/> and coordination number,  <img src=\"http://latex.codecogs.com/svg.latex?CN_i\" border=\"0\"/>: <img src=\"http://latex.codecogs.com/svg.latex?r_i = f(n_i, CN_i)\" border=\"0\"/>.",
                    "<br /><br />",
                    "Given a new formula, <img src=\"http://latex.codecogs.com/svg.latex?CC'X_3\" border=\"0\"/> where <img src=\"http://latex.codecogs.com/svg.latex?C\" border=\"0\"/> and <img src=\"http://latex.codecogs.com/svg.latex?C'\" border=\"0\"/> are cations and we don't yet know which is <img src=\"http://latex.codecogs.com/svg.latex?A\" border=\"0\"/> and which is <img src=\"http://latex.codecogs.com/svg.latex?B\" border=\"0\"/>, we apply the following scheme:",
                    "",
                    "<br />",
                    "1) A list of allowed <img src=\"http://latex.codecogs.com/svg.latex?n\" border=\"0\"/> is defined for <img src=\"http://latex.codecogs.com/svg.latex?C\" border=\"0\"/> and <img src=\"http://latex.codecogs.com/svg.latex?C'\" border=\"0\"/> based on the set of <img src=\"http://latex.codecogs.com/svg.latex?n_i\" border=\"0\"/> such that <img src=\"http://latex.codecogs.com/svg.latex?r_i(n_i)\" border=\"0\"/> exists within Shannon's data.",
                    "",
                    "<br />2) All pairs of oxidation states <img src=\"http://latex.codecogs.com/svg.latex?(n_C, n_{C'})\" border=\"0\"/> that charge-balance <img src=\"http://latex.codecogs.com/svg.latex?X_3\" border=\"0\"/> are considered, where <img src=\"http://latex.codecogs.com/svg.latex?n_X\" border=\"0\"/> is typically known <i>a priori</i> (<i>e.g.</i>, <img src=\"http://latex.codecogs.com/svg.latex?n_O = 2^-\" border=\"0\"/>). ",
                    "",
                    "<br />3) In the infrequent case where more than one charge-balanced pair exists, a single solution is chosen based on the electronegativity ratio of the two cations, <img src=\"http://latex.codecogs.com/svg.latex?\\chi_C/\\chi_{C'}\" border=\"0\"/>. If <img src=\"http://latex.codecogs.com/svg.latex?0.9 < \\chi_C/\\chi_{C'} < 1.1\" border=\"0\"/>, the pair that minimizes <img src=\"http://latex.codecogs.com/svg.latex?|n_C-n_{C'}|\" border=\"0\"/> is chosen, otherwise, the pair that maximizes <img src=\"http://latex.codecogs.com/svg.latex?|n_C-n_{C'}|\" border=\"0\"/> is chosen. ",
                    "",
                    "<br />4) With <img src=\"http://latex.codecogs.com/svg.latex?(n_C, n_{C'})\" border=\"0\"/> determined, the radii of each cation if they were to sit on <img src=\"http://latex.codecogs.com/svg.latex?A\\/(CN = 12)\" border=\"0\"/> or  <img src=\"http://latex.codecogs.com/svg.latex?B\\/(CN = 6)\" border=\"0\"/>is generated using Shannon's table.",
                    "",
                    "<br />5) The determination of which cation is larger, and therefore the <img src=\"http://latex.codecogs.com/svg.latex?A\" border=\"0\"/>-site, is made by systematically comparing these radii.",
                    "",
                    "This strategy reproduces the assignment of the <img src=\"http://latex.codecogs.com/svg.latex?A\"/> and <img src=\"http://latex.codecogs.com/svg.latex?B\"/> cations for 100% of the 313 experimentally labeled <img src=\"http://latex.codecogs.com/svg.latex?ABX_3\" border=\"0\"/> perovskites in the set of 576 used to determine <img src=\"http://latex.codecogs.com/svg.latex?\\tau\" border=\"0\"/>. Conveniently, this approach naturally yields <img src=\"http://latex.codecogs.com/svg.latex?{n_A,n_B,n_X,r_A,r_B,r_X}\" border=\"0\"/> which are the inputs to <img src=\"http://latex.codecogs.com/svg.latex?\\tau\" border=\"0\"/> and <img src=\"http://latex.codecogs.com/svg.latex?t\" border=\"0\"/>."
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                    "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<legend>On assigning oxidation states and radii</legend>\n\n<img src=\"http://latex.codecogs.com/svg.latex?t\" border=\"0\"> and <img src=\"http://latex.codecogs.com/svg.latex?\\tau\" border=\"0\"> are both functions of the site-specific radii, <img src=\"http://latex.codecogs.com/svg.latex?r_i\" border=\"0\">, yet it is not known <i>a priori</i> for a new formula which cation will sit on the 12-fold coordinated <img src=\"http://latex.codecogs.com/svg.latex?A\" border=\"0\">-site and 6-fold coordinted <img src=\"http://latex.codecogs.com/svg.latex?B\" border=\"0\">-site. To address this, we developed a systematic approach for making this determination based on the condition that <img src=\"http://latex.codecogs.com/svg.latex?r_A &gt; r_B\" border=\"0\"> because of the larger coordination number in the perovskite structure.\n\nIonic radii have been developed by a number of researchers in the last 100 years, but R.D. Shannon's set [<a href=\"http://scripts.iucr.org/cgi-bin/paper?S0567739476001551\">link</a>] are regarded as the most comprehensive, provided as a function of both oxidation state, <img src=\"http://latex.codecogs.com/svg.latex?n_i\" ,=\"\" border=\"0\"> and coordination number,  <img src=\"http://latex.codecogs.com/svg.latex?CN_i\" border=\"0\">: <img src=\"http://latex.codecogs.com/svg.latex?r_i = f(n_i, CN_i)\" border=\"0\">.\n<br><br>\nGiven a new formula, <img src=\"http://latex.codecogs.com/svg.latex?CC'X_3\" border=\"0\"> where <img src=\"http://latex.codecogs.com/svg.latex?C\" border=\"0\"> and <img src=\"http://latex.codecogs.com/svg.latex?C'\" border=\"0\"> are cations and we don't yet know which is <img src=\"http://latex.codecogs.com/svg.latex?A\" border=\"0\"> and which is <img src=\"http://latex.codecogs.com/svg.latex?B\" border=\"0\">, we apply the following scheme:\n\n<br>\n1) A list of allowed <img src=\"http://latex.codecogs.com/svg.latex?n\" border=\"0\"> is defined for <img src=\"http://latex.codecogs.com/svg.latex?C\" border=\"0\"> and <img src=\"http://latex.codecogs.com/svg.latex?C'\" border=\"0\"> based on the set of <img src=\"http://latex.codecogs.com/svg.latex?n_i\" border=\"0\"> such that <img src=\"http://latex.codecogs.com/svg.latex?r_i(n_i)\" border=\"0\"> exists within Shannon's data.\n\n<br>2) All pairs of oxidation states <img src=\"http://latex.codecogs.com/svg.latex?(n_C, n_{C'})\" border=\"0\"> that charge-balance <img src=\"http://latex.codecogs.com/svg.latex?X_3\" border=\"0\"> are considered, where <img src=\"http://latex.codecogs.com/svg.latex?n_X\" border=\"0\"> is typically known <i>a priori</i> (<i>e.g.</i>, <img src=\"http://latex.codecogs.com/svg.latex?n_O = 2^-\" border=\"0\">). \n\n<br>3) In the infrequent case where more than one charge-balanced pair exists, a single solution is chosen based on the electronegativity ratio of the two cations, <img src=\"http://latex.codecogs.com/svg.latex?\\chi_C/\\chi_{C'}\" border=\"0\">. If <img src=\"http://latex.codecogs.com/svg.latex?0.9 &lt; \\chi_C/\\chi_{C'} &lt; 1.1\" border=\"0\">, the pair that minimizes <img src=\"http://latex.codecogs.com/svg.latex?|n_C-n_{C'}|\" border=\"0\"> is chosen, otherwise, the pair that maximizes <img src=\"http://latex.codecogs.com/svg.latex?|n_C-n_{C'}|\" border=\"0\"> is chosen. \n\n<br>4) With <img src=\"http://latex.codecogs.com/svg.latex?(n_C, n_{C'})\" border=\"0\"> determined, the radii of each cation if they were to sit on <img src=\"http://latex.codecogs.com/svg.latex?A\\/(CN = 12)\" border=\"0\"> or  <img src=\"http://latex.codecogs.com/svg.latex?B\\/(CN = 6)\" border=\"0\">is generated using Shannon's table.\n\n<br>5) The determination of which cation is larger, and therefore the <img src=\"http://latex.codecogs.com/svg.latex?A\" border=\"0\">-site, is made by systematically comparing these radii.\n\nThis strategy reproduces the assignment of the <img src=\"http://latex.codecogs.com/svg.latex?A\"> and <img src=\"http://latex.codecogs.com/svg.latex?B\"> cations for 100% of the 313 experimentally labeled <img src=\"http://latex.codecogs.com/svg.latex?ABX_3\" border=\"0\"> perovskites in the set of 576 used to determine <img src=\"http://latex.codecogs.com/svg.latex?\\tau\" border=\"0\">. Conveniently, this approach naturally yields <img src=\"http://latex.codecogs.com/svg.latex?{n_A,n_B,n_X,r_A,r_B,r_X}\" border=\"0\"> which are the inputs to <img src=\"http://latex.codecogs.com/svg.latex?\\tau\" border=\"0\"> and <img src=\"http://latex.codecogs.com/svg.latex?t\" border=\"0\">."
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                    "<legend>Formulas with these cations (A, B) and anions (X) were used to identify <img src=\"http://latex.codecogs.com/svg.latex?\\tau\" border=\"0\"/></legend>",
                    "The utility will allow the use of some other cations and anions, but these have not been as thoroughly tested.",
                    "<br /><br />",
                    "",
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