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ASR_model/RandomForestRegressor.pkl

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ASR_model/StandardScaler.pkl

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Barrier_model/RandomForestRegressor.pkl

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Barrier_model/StandardScaler.pkl

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Stability_model/RandomForestRegressor.pkl

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Stability_model/StandardScaler.pkl

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model_predict_df.py

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+import os
+import pandas as pd
+import numpy as np
+import joblib
+from mastml.feature_generators import ElementalFeatureGenerator, OneHotGroupGenerator
+from pymatgen.analysis.cost import CostAnalyzer, CostDBElements
+
+def get_cost(comp_list):
+    ca = CostAnalyzer(costdb=CostDBElements())
+    costs = list()
+    for comp in comp_list:
+        cost = ca.get_cost_per_kg(comp=comp)
+        costs.append(cost)
+    return costs
+
+def get_stability(df_test):
+    d = 'ASR_model/Stability_model'
+    scaler = joblib.load(os.path.join(d, 'StandardScaler.pkl'))
+    model = joblib.load(os.path.join(d, 'RandomForestRegressor.pkl'))
+    df_features = pd.read_csv(os.path.join(d, 'X_train.csv'))
+
+    features = df_features.columns.tolist()
+    df_test2 = df_test[features]
+    X_stab = scaler.transform(df_test2)
+    stabilities = model.predict(X_stab)
+
+    return stabilities
+
+def get_barrier(df_test):
+    d = 'ASR_model/Barrier_model'
+    scaler = joblib.load(os.path.join(d, 'StandardScaler.pkl'))
+    model = joblib.load(os.path.join(d, 'RandomForestRegressor.pkl'))
+    df_features = pd.read_csv(os.path.join(d, 'X_train.csv'))
+
+    features = df_features.columns.tolist()
+    X_barrier = df_test[features]
+    X_barrier = scaler.transform(X_barrier)
+    barriers = model.predict(X_barrier)
+
+    return barriers
+
+def get_asr(df_test):
+    d = 'ASR_model/ASR_model'
+    scaler = joblib.load(os.path.join(d, 'StandardScaler.pkl'))
+    model = joblib.load(os.path.join(d, 'RandomForestRegressor.pkl'))
+    df_features = pd.read_csv(os.path.join(d, 'X_train.csv'))
+
+    features = df_features.columns.tolist()
+    df_test = df_test[features]
+
+    X_ASR = scaler.transform(df_test)
+
+    asrs = model.predict(X_ASR)
+
+    # Get ebars and recalibrate them
+    a = 0.42824232546669644
+    b = 0.36341790743237223
+    errs_list = list()
+    for i, x in X_ASR.iterrows():
+        preds_list = list()
+        for pred in model.model.estimators_:
+            preds_list.append(pred.predict(np.array(x).reshape(1, -1))[0])
+        errs_list.append(np.std(preds_list))
+    asr_ebars = a * np.array(errs_list) + b
+
+    return asrs, asr_ebars
+
+def process_data(comp_list, elec_list):
+    X = pd.DataFrame(np.empty((len(comp_list),)))
+    y = pd.DataFrame(np.empty((len(comp_list),)))
+
+    df_test = pd.DataFrame({'Material composition': comp_list})
+
+    # Try this both ways depending on mastml version used.
+    try:
+        X, y = ElementalFeatureGenerator(composition_df=df_test['Material composition'],
+                                    feature_types=['composition_avg', 'arithmetic_avg', 'max', 'min','difference'],
+                                    remove_constant_columns=False).evaluate(X=X, y=y, savepath=os.getcwd(), make_new_dir=False)
+    except:
+        X, y = ElementalFeatureGenerator(featurize_df=df_test['Material composition'],
+                                         feature_types=['composition_avg', 'arithmetic_avg', 'max', 'min',
+                                                        'difference'], remove_constant_columns=False).evaluate(X=X, y=y, savepath=os.getcwd(), make_new_dir=False)
+
+    df_test = pd.concat([df_test, X], axis=1)
+
+    elec_cls_0 = list()
+    elec_cls_1 = list()
+    elec_cls_2 = list()
+    elec_cls_3 = list()
+    for elec in elec_list:
+        if elec == 'ceria':
+            elec_cls_0.append(1)
+            elec_cls_1.append(0)
+            elec_cls_2.append(0)
+            elec_cls_3.append(0)
+        elif elec == 'mixed':
+            elec_cls_0.append(0)
+            elec_cls_1.append(1)
+            elec_cls_2.append(0)
+            elec_cls_3.append(0)
+        elif elec == 'perovskite':
+            elec_cls_0.append(0)
+            elec_cls_1.append(0)
+            elec_cls_2.append(1)
+            elec_cls_3.append(0)
+        elif elec == 'zirconia':
+            elec_cls_0.append(0)
+            elec_cls_1.append(0)
+            elec_cls_2.append(0)
+            elec_cls_3.append(1)
+        else:
+            raise ValueError('Invalid electrolyte choice detected. Valid choices are "ceria", "mixed", "perovskite", "zirconia"')
+
+    df_test['Electrolyte class_0'] = elec_cls_0  # ceria
+    df_test['Electrolyte class_1'] = elec_cls_1  # mixed
+    df_test['Electrolyte class_2'] = elec_cls_2  # perovskite
+    df_test['Electrolyte class_3'] = elec_cls_3  # zirconia
+
+    return df_test
+
+def make_predictions(comp_list, elec_list):
+
+    # Check comp and elec list lengths match
+    assert len(comp_list) == len(elec_list)
+
+    # Process data
+    df_test = process_data(comp_list, elec_list)
+
+    # Calculate the cost of the materials
+    costs = get_cost(comp_list)
+
+    # Get the ML-predicted stability of the materials
+    stabilities = get_stability(df_test)
+
+    # Get the ML-predicted ASR barrier of the materials
+    barriers = get_barrier(df_test)
+
+    df_test['ML pred ASR barrier (eV)'] = barriers
+    asrs, asr_ebars = get_asr(df_test)
+
+    pred_dict = {'Compositions': comp_list,
+                 'Electrolytes': elec_list,
+                 'Cost ($/kg)': costs,
+                 'Stability @ 500C (meV/atom)': stabilities,
+                 'ASR barrier (eV)': barriers,
+                 'log ASR at 500C (Ohm-cm2)': asrs,
+                 'log ASR error (Ohm-cm2)': asr_ebars}
+
+    return pd.DataFrame(pred_dict)
+