Predicting band gaps and band-edge positions of oxide perovskites using DFT and machine learning

Abstract

Density functional theory within the local or semilocal density approximations (DFT-LDA/GGA) has become a workhorse in electronic structure theory of solids, being extremely fast and reliable for energetics and structural properties, yet remaining highly inaccurate for predicting band gaps of semiconductors and insulators. Accurate prediction of band gaps using firstprinciples methods is time consuming, requiring hybrid functionals, quasi-particle GW, or quantum Monte Carlo methods. Efficiently correcting DFT-LDA/GGA band gaps and unveilling the main chemical and structural factors involved in this correction is desirable for discovering novel materials in high-throughput calculations. In this direction, we use DFT and machine learning techniques to correct band gaps and band-edge positions of a repre-1 sentative subset of ABO 3 perovskite oxides. Relying on results of HSE06 hybrid functional calculations as target values of band gaps, we find a systematic band gap correction of ∼1.5 eV for this class of materials, where ∼1 eV comes from downward shifting the valence band and ∼0.5 eV from uplifting the conduction band. The main chemical and structural factors determining the band gap correction are determined through a feature selection procedure.

Predicting Band Gap of Transition Metal Trihalides using Machine Learning

“…The other two models, GBRT, and RFR are ensemble types of ML models, which worked based on trees. Since they are tree-based models normalization of the data is not mandatory [34]. We have split the data as train and test set, 90 % of data for the training set and 10 % of data for the test set.…”