Prediction of dielectric constants of ABO-type perovskites using machine learning and first-principles calculations

In this study, the machine-learning method, combined with density functional perturbation theory (DFPT) calculations, was implemented to predict and validate the dielectric constants of ABO 3 -type perovskites. For the construction of the training database, the dielectric constants of 7113 inorganic...

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Published in:Physical chemistry chemical physics : PCCP 2022-03, Vol.24 (11), p.75-759
Main Authors: Kim, Eunsong, Kim, Joonchul, Min, Kyoungmin
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Summary:In this study, the machine-learning method, combined with density functional perturbation theory (DFPT) calculations, was implemented to predict and validate the dielectric constants of ABO 3 -type perovskites. For the construction of the training database, the dielectric constants of 7113 inorganic materials were extracted from the Materials Project. The chemical, structural, and physical descriptors were generated and trained using the gradient-boosting-based regressor after feature engineering. The prediction accuracies were 0.83 and 0.67 ( R 2 ) and 0.12 and 0.26 (root mean square error) for the electronic and ionic contributions to the dielectric constant, respectively. The constructed surrogate model was then employed to predict the dielectric constants of the ABO 3 -type perovskites (216 structures), whose thermodynamic stabilities were satisfactory. The predicted values were validated using DFPT calculations. The constructed database was further used to develop a surrogate model for the prediction of dielectric constants. The final R 2 prediction accuracies reached 0.79 (electronic) and 0.67 (ionic). We constructed the machine-learning model for predicting the dielectric constant of ABO 3 perovskite with additionally calculated and analyzed 216 thermodynamically stable ABO 3 -type perovskites.
ISSN:1463-9076
1463-9084
DOI:10.1039/d1cp04702g