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

TL;DR

This paper combines machine learning and density functional theory to predict and validate new lead-free halide perovskite materials suitable for solar cells, demonstrating a more efficient approach for material discovery.

Contribution

It introduces a novel machine learning-based methodology integrated with DFT calculations for discovering environmentally friendly perovskite materials.

Findings

Seven new halide perovskite materials predicted.

CsMnCl₄ exhibits a suitable bandgap of 1.37 eV.

Validated the effectiveness of combined ML and DFT approach.

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.