  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.
