--- library_name: sklearn tags: - tabular-regression - materials property prediction - baseline-trainer widget: structuredData: Sc: - 0 Ti: - 0 V: - 0 Cr: - 0 Mn: - 0 Fe: - 12.0 Co: - 2.0 Ni: - 0 Cu: - 0 Al: - 0 Si: - 0 Ga: - 0 Ge: - 0 Be: - 0 Nb: - 0 Mo: - 0 Re: - 0 Ru: - 0 La: - 0 Ce: - 0 Pr: - 1.9 Nd: - 0 Sm: - 0 Eu: - 0 Gd: - 0 Tb: - 0.1 Dy: - 0 Ho: - 0 Er: - 0 Tm: - 0 Yb: - 0 Lu: - 0 Th: - 0 Y: - 0 Zr: - 0 B: - 0 C: - 0 --- **Model Description** The magnet Curie temperature (Tc [K]) predictor model has been trained using a supervised learning approach on a specific set of magnet classes having 14:2:1 phases. The dataset to train the Tc prediction model is a distinct literature source. Further, the Tc values for various 14:2:1 magnet phases at room temperature are considered for dataset creation. It predicts the Tc value using the chemical composition as a feature. E.g: To predict the Tc value Nd2Fe14B1 magnet composition, the features are Nd=2, Fe=14, and B=1. **Application & Limitations** The trained model is valid for 14:2:1 phases only, which are stoichiometric compositions and the predicted Tc value is in Kelvin and at room temperature. **Model pipeline** The voting regressor to predict the Tc combines four base models. 
VotingRegressor(estimators=[('ET', ExtraTreesRegressor()),
                            ('XGB',
                             XGBRegressor(alpha=0.5, base_score=0.5,
                                          booster='gbtree', colsample_bylevel=1,
                                          colsample_bynode=1,
                                          colsample_bytree=0.4,
                                          enable_categorical=False, gamma=0,
                                          gpu_id=-1, importance_type=None,
                                          interaction_constraints='',
                                          learning_rate=0.2, max_delta_step=0,
                                          max_depth=2, min_child_weight=1,
                                          missing=nan,
                                          mo...
                                          n_estimators=1000, n_jobs=8,
                                          num_parallel_tree=1, predictor='auto',
                                          random_state=0, reg_alpha=0.5,
                                          reg_lambda=1, scale_pos_weight=1,
                                          subsample=1, tree_method='exact',
                                          validate_parameters=1,
                                          verbosity=None)),
                            ('RF', RandomForestRegressor(max_depth=100)),
                            ('AB',
                             AdaBoostRegressor(base_estimator=RandomForestRegressor(max_depth=50,
                                                                                    n_estimators=50),
                                               learning_rate=0.001))])