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

Upendra Kumar; Hyeon Woo Kim; Gyanendra Kumar Maurya; Bincy Babu Raj; Sobhit Singh; Ajay Kumar Kushwaha; Sung Beom Cho; Hyunseok Ko

paper

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

arXiv:2407.15573

Abstract

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.