Predicting Sulfur‐Rich Oxysulfide Perovskites for Water‐Splitting Applications Using Machine Learning

Abstract Solar energy offers a promising means of addressing energy supply and storage problems, but this potential is not fully realized due to a lack of suitable semiconducting materials. The discovery of new materials with desirable properties has historically been conducted either using an experimental or a first‐principles density functional theory based study. These approaches are extremely time‐intensive, and therefore, cannot be applied effectively to study a large number of systems. In such situations, machine learning can be used to make predictions about properties of new compounds from known data, providing a more efficient route to materials discovery. Here, this approach is used to predict the bandgap of a series of oxysulfide perovskites (of the form of ABO X S 3− X , X = 0,1,2,3), in general, and sulfur‐rich ABOS 2 , in particular. Atomic properties of constituent elements in the perovskite structures via 1.048 millions possible subsets of features are employed to train the models. Further, feature selection, kernel ridge regression, and k ‐nearest neighbors classification methods are applied to downselect the promising ABOS 2 based oxysulfide perovskites for water‐splitting. The accuracy of each model is determined using standard statistical metrics. Finally, seven stable but yet unsynthesized sulfur‐rich oxysulfide perovskites (BiInOS 2 , BiGaOS 2 , SbInOS 2 , SbGaOS 2 , SbAlOS 2 , SnZrOS 2 , and MgSnOS 2 ) that show potential for water‐splitting applications are proposed.