Machine Learning-Enhanced Design of Lead-Free Halide Perovskite Materials Using Density Functional Theory

The investigation of emerging non-toxic perovskite materials has been undertaken to advance the fabrication of environmentally sustainable lead-free perovskite solar cells. This study introduces a machine learning methodology aimed at predicting innovative halide perovskite materials that hold promise for use in photovoltaic applications. The seven newly predicted materials are as follows: CsMnCl${}_4$, Rb${}_3$Mn${}_2$Cl${}_9$, Rb${}_4$MnCl${}_6$, Rb${}_3$MnCl${}_5$, RbMn${}_2$Cl${}_7$, RbMn${}_4$Cl${}_9$, and CsIn${}_2$Cl${}_7$. The predicted compounds are first screened using a machine learning approach, and their validity is subsequently verified through density functional theory calculations. CsMnCl${}_4$ is notable among them, displaying a bandgap of 1.37 eV, falling within the Shockley-Queisser limit, making it suitable for photovoltaic applications. Through the integration of machine learning and density functional theory, this study presents a methodology that is more effective and thorough for the discovery and design of materials.