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.
Upendra Kumar would like to express his sincere gratitude to Nitish Singh, the founder of Campus X, for generously providing a wealth of free and affordable resources for studying machine learning. These materials have been incredibly helpful to him in his learning journey. This study was primarly supported by Virtual Engineering Platform Project (Grant No. P0022336), funded by the Ministry of Trade, Industry and Energy (MoTIE) South Korea and National Research Foundation of Korea (RS-2023-00285390 and RS-2023-00209910). Ajay Kumar Kushwaha acknowledges the financial support (under Grant No. 03(1460)/19/EMR-II.) of CSIR, New Delhi. Sobhit Singh acknowledges the support provided by the U.S. Department of Energy, Office of Science, Office of Fusion Energy Sciences, through the Quantum Information Science program under Award No. DE-SC0020340.This study was supported by National Research Foundation of Korea (NRF-2020M3H4A3081796).
