Substitution engineering of lead-free halide perovskites for photocatalytic applications assisted by machine learning

Abstract

Lead-free perovskites (A₃B₂X₉) have drawn much attention in recent years. However, a thorough understanding of these materials is still in its early stages. This is because A₃B₂X₉ perovskites have large-scale component tunability, in which the A⁺, B³⁺, and X⁻ ions can be replaced or partially substituted with other elements. Here, based on density functional theory and machine learning techniques we propose a data-driven method to find suitable configurations for photocatalytic water splitting. By replacing atoms in A₃B₂X₉, 3.4 million configurations are constructed and studied. Our results show that the substitutional position plays an important role in determining the photocatalytic performance. Specifically, the co-existence of Br and I elements is favorable for X-sites, while for B-site atoms, it is better to choose atoms from groups IIIB and IIIA with a period greater than 3. Considering their rarity and toxicity, we believe that In is a good choice for B-sites and propose CsRb₂BiInBr₅I₄ as a promising candidate. These results may provide guidance for the discovery of novel lead-free perovskites for photocatalytic applications.