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Density functional theory within the local or semilocal densityapproximations (DFT-LDA/GGA) has become a workhorse in electronic structuretheory of solids, being extremely fast and reliable for energetics andstructural properties, yet remaining highly inaccurate for predicting band gapsof semiconductors and insulators. Accurate prediction of band gaps usingfirstprinciples methods is time consuming, requiring hybrid functionals,quasi-particle GW, or quantum Monte Carlo methods. Efficiently correctingDFT-LDA/GGA band gaps and unveiling the main chemical and structural factorsinvolved in this correction is desirable for discovering novel materials inhigh-throughput calculations. In this direction, we use DFT and machinelearning techniques to correct band gaps and band-edge positions of arepresentative subset of ABO3 perovskite oxides. Relying on results of HSE06hybrid functional calculations as target values of band gaps, we find asystematic band gap correction of ~1.5 eV for this class of materials, where ~1eV comes from downward shifting the valence band and ~0.5 eV from uplifting theconduction band. The main chemical and structural factors determining the bandgap correction are determined through a feature selection procedure.

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