Machine learning accelerated search for new double perovskite oxide photocatalysis

Abstract

Double perovskite oxide <i>A</i>₂<i>BB'</i>O₆ has better stability and wider bandgap range than <i>AB</i>O₃-type oxide, and exhibits great prospects in photocatalytic overall water splitting. However, owing to the diversity of crystal structure and constituents of perovskite oxide, rapidly and accurately searching for <i>A</i>₂<i>BB'</i>O₆ for photocatalyst is still a big challenge, both experimentally and theoretically. In this work, in order to screen out suitable double perovskite oxide photocatalysts, a multi-step framework combined with machine learning technique and first-principles calculations is proposed. Nearly 8000 candidates with proper bandgaps for water splitting are screened out from among more than 50000 <i>A</i>₂<i>BB'</i>O₆-type double perovskite oxides. Statistical analysis of the results shows that double perovskite oxides with d¹⁰ metal ions at <i>B/B</i><i><i>'</i></i> sites are more likely to have good absorption of visible light, and the structural symmetry of double perovskite also has influence on the bandgap value. Furthermore, first-principles calculations demonstrate that Sr₂GaSbO₆, Sr₂InSbO₆ and K₂NbTaO₆ are non-toxic photocatalyst candidates with proper band edges for overall water splitting.