Data-driven optimization and machine learning analysis of compatible molecules for halide perovskite material

Abstract

Abstract Optoelectronic stability of halide perovskite material in hostile conditions such as water is rather limited, preventing them from further industrial deployment. Here, we optimize and perform machine learning analysis on CH3NH3PbI3 materials with additives, solvents and post-treatment molecules using combined experimental and data-driven methods. A champion system consisting of a compatible tertiary molecular combination ‘calcein+PbBr2 + DMSO’ active at diverse surfaces is identified, delivering a large aqueous photoelectrochemical (PEC) photocurrent of 10-5 A/cm2 and an improved aqueous stability of 92.5%. Subsequently, machine interpretation is provided to decouple the multi-molecule contributions with the assistance of genetic programming (GP) and extra-trees (ET) machine learning models, highlighting the intricate molecular features for the target outputs. The post-hoc density functional theory (DFT) calculation suggests the presence of multiple hydrogen bond and anion··π surface interactions to stabilize the interfacial structures. The present ‘PEC + GP + ET + DFT’ approach is suggested to be an effective approach to design and comprehensively evaluate molecule-modified materials.