Machine Learning Augmented Discovery of Chalcogenide Double Perovskites for Photovoltaics

Abstract

Abstract Hybrid organic inorganic perovskite solar cells based on CH 3 NH 3 PbI 3 have drastically increased in efficiency over the past several years and are competitive with decades‐old photovoltaic materials such as CdTe. Despite this impressive increase, significant issues still remain due to the intrinsic instability of CH 3 NH 3 PbI 3 which degrades into carcinogenic PbI 2 . Recently, double halide perovskites which use a pair of 1 + –3 + cations to replace Pb 2+ , such as Cs 2 InSbI 6 , and chalcogenide perovskites, such as BaZrS 3 , have been explored as potential replacements. In this work, double chalcogenide perovskites are explored to identify novel photovoltaic absorbers that can replace CH 3 NH 3 PbI 3 . Due to the large space of possible compounds, machine learning methods are used to classify materials as potential photovoltaic absorbers using data from the periodic table, eliminating wasteful computation. A random forest algorithm achieves a cross‐validation accuracy of 86.4% on the constructed data set. Over 450 possible replacements are identified via traditional and statistical methods with Ba 2 AlNbS 6 , Ba 2 GaNbS 6 , Ca 2 GaNbS 6 , Sr 2 InNbS 6 , and Ba 2 SnHfS 6 as the most promising alternative when thermodynamic stability, kinetic stability, and optical absorption are considered.