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

2024/07/22 by Kumar, Upendra, Kim, Hyeon Woo, Maurya, Gyanendra Kumar +5
#FOS: Physical sciences #Materials Science (cond-mat.mtrl-sci)

paper · doi:10.48550/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: CsMnCl₄, Rb₃Mn₂Cl₉, Rb₄MnCl₆, Rb₃MnCl₅, RbMn₂Cl₇, RbMn₄Cl₉, and CsIn₂Cl₇. The predicted compounds are first screened using a machine learning approach, and their validity is subsequently verified through density functional theory calculations. CsMnCl₄ 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.

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