Machine learning-enhanced design of lead-free halide perovskite materials using density functional theory - Kumar, Upendra; Kim, Hyeon Woo; Maurya, Gyanendra Kumar; Raj, Bincy Babu; Singh, Sobhit; Kushwaha, Ajay Kumar; Cho, Sung Beom; Ko, Hyunseok (Current Applied Physics, 2025)
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.
Keywords: Density-functional theory calculations; Density-functional-theory; Halide perovskite material; Halide perovskites; Lead-Free; Lead-free perovskites; Machine learning approaches; Machine-learning; Non-toxic; Photovoltaic applications; Density functional theory; Halide perovskite materials; Machine learning; Photovoltaic application