Prediction of Aqueous Stable Lead-Free Hybrid Halide Perovskites for Efficient Solar Water Splitting Using Machine Learning and Molecular Dynamics

Abstract

Hybrid organic-inorganic halide perovskites (HOIP), have garnered significant attention in many opto-electronic applications due to their high efficiency and tuneable bandgaps. However, their application in the field of solar water splitting remains largely unexplored, attributed to their instability in aqueous environments and the fact that their valence and conduction band edges fail to straddle the redox potential of water, which prevents unbiased solar water splitting. To address this, various Machine Learning (ML) regression models are employed in this work to predict the band gap and band edge values for 3D-HOIPs that are critical for solar water splitting application. Application-driven screening identified 21 potential leadfree perovskites with statistically calculated STH efficiency ≥10%. In addition, Density Functional Theory (DFT) and Ab Initio Molecular Dynamics (AIMD) simulation highlighted the structural and aqueous stability of the selected HOIP, namely FmSnI2Br, with bandgap close to ML predicted value, making it suitable for water splitting applications.