Machine Learning Models for Efficient Property Prediction of ABX3 Materials: A High-Throughput Approach

Abstract

Recently, ABX₃ materials have garnered significant attention due to their diverse applications in photovoltaics, catalysis, and optoelectronics as well as their remarkable efficiency in energy conversion. However, progress has been somewhat slow due to the high expenses of the experiment or the time-consuming density functional theory (DFT) calculation. In this study, we utilized the extreme gradient boosting (XGBoost) algorithm to facilitate the discovery and characterization of ABX₃ compounds based on vast data sets generated by DFT calculations. While the XGBoost algorithm provides a powerful tool for accelerating the discovery of ABX₃ compounds, it is crucial to acknowledge that different DFT approximation levels can significantly impact the predicted band gaps, potentially introducing discrepancies when compared with experimental values. In the first step, we predict the space group of 13947 oxides and halides using the Open Quantum Materials Database and elemental features. Our analysis yields classification accuracies ranging from 82.39% to 99.14% across these materials. Following this, XGBoost regression algorithms are employed to interrogate the data set, enabling predictions of volume (achieving an optimal accuracy of 98.41%, with a mean absolute error (MAE) of 2.395 ų and a root-mean-square error (RMSE) of 4.416 ų), formation energy (an optimal accuracy of 97.36%, with an MAE of 0.075 eV/atom and an RMSE of 0.132 eV/atom), and band gap energy (an optimal accuracy of 87.00%, an MAE of 0.391 eV, and an RMSE of 0.574 eV). Finally, these prediction models are employed to identify the possible space groups for each of the 1252 new ABX₃ formulas. Then, we predict the volume, the formation energy, and the band gap energy for each candidate space group. Through these predictive models, machine learning accelerates the exploration of new materials with enhanced performance and functionality.