Rapid Discovery of Ferroelectric Photovoltaic Perovskites and Material Descriptors via Machine Learning

Rapid discovery of novel functional materials is urgent but a tremendous challenge using trial-and-error methods in vast chemical space. Here, a multistep screening scheme is developed by combining high-throughput calculations and machine learning (ML) techniques. Successfully, 151 promising stable ferroelectric photovoltaic (FPV) perovskites with proper bandgap are screened out from 19 841 candidate compositions. Two new descriptors are proposed to describe mixed inorganic perovskites' formability through ML feature engineering. Additionally, phase-transition energy difference is used as a criterion for directly judging whether the compound can expose spontaneous polarization. The ML prediction accuracy of both energy difference and bandgap regressions is over 90% and ML produces comparable results to density functional theory calculations. Moreover, bandgaps of eight selected FPV perovskites are all close to the optimal value of single-junction solar cells. This scheme not only realizes the ML acceleration for targeted multiproperty materials' design and expansion of materials database, but also opens a way for descriptor development.