High-throughput calculation and machine learning of two-dimensional halide perovskite materials: Formation energy and band gap

Abstract

Both band gap and stability of halide perovskites are prerequisites for deployable photovoltaic devices; however, many machine learning researches focus on one target output and a systematic machine learning workflow for achieving multiple targets is desirable. In this manuscript, we employ machine learning (ML) coupled with high-throughput density functional theory (DFT) calculation to predict potential two-dimensional lead-free halide perovskite materials with appropriate band gap and stability for solar cell applications. This is realized by the construction of two machine learning models based on the random forest algorithm with each targeting on band gap or formation energy, followed by the candidate intersection for the materials screening. The multi-objective DFT+ML framework predicts three possible lead-free two-dimensional halide perovskite materials with suitable stability and band gap, which are further evaluated via the molecular dynamics to evaluate their thermodynamic stability. Their spectroscopic limited maximum efficiencies (SLMEs) are calculated to confirm their photovoltaic capabilities. In order to comprehensively evaluate the features, new descriptors for the halide perovskite materials with better correlation with the target output are automatically formulated via symbolic regression based on genetic algorithms, and an alternative feature analysis method based on the literature textual data and natural language processing (NLP) is proposed. Post hoc analysis is performed via DFT and molecular dynamics to provide more detailed information on the materials prediction. This study highlights the developments multi-objective machine learning workflow for inverse materials design and analysis.