(376g) Analyzing the Bandgap of 3D Perovskite Oxides: Machine Learning Approach

In this study we have developed a machine learning approach to analyze and predict the bandgaps of 1146 ABO₃ perovskites, computed using density functional theory. Since DFT-computed bandgaps with high accuracy are normally also highly resource demanding, we developed a machine learning model to predict the perovskite bandgaps by utilizing readily available atomic and crystal descriptors. These descriptors include atomic electrochemical properties such as electron affinity and ionization energy of the A/B-site atoms, as well as Goldschmidt tolerance factor of the crystal structure. As we arrive at our results, we conduct a comparative study of two machine learning algorithms: namely kernel ridge regression and gradient boosting regression. Along the way, we employ recursive feature elimination and correlation based feature selection methods to remove the weakest inputs from our dataset, and reduce the 35 starting features down to 28 most essential features, resulting in a final model mean square error of 0.2 eV. Lastly, we performed a relative contribution analysis of the inputs with regards to their influence on the bandgap of the perovskites. In essence, we attempt to use machine learning as an analytical technique for evaluating the structure-bandgap relationship of perovskite.