First-principles based machine learning study of oxygen evolution reactions of perovskite oxides using a surface center-environment feature model

Abstract

Perovskite-type oxide ABO3 catalysts can be used to prepare inexpensive, efficient, durable, and environmentally friendly electrochemical energy conversion devices. The numerous combinatorial substitutions of perovskite oxides provide rich choices of candidates but pose a great challenge to examine all the possibilities both experimentally and computationally. Inspired by the data-driven materials design approach, we proposed a surface center-environment (SCE) feature model and developed first-principles based machine learning (ML) methods to predict the adsorption free energies of intermediate species (HO*, O*, and HOO*) on the surfaces of perovskite oxides and their overpotentials, critical to evaluating the catalytic performance of oxygen evolution reaction (OER). The SCE feature models contain the elementary properties of substitution elements and the structure information of the perovskite surfaces, demonstrated as an effective description of catalytic reactions on surface in ML models. The ML models predicted that the perovskite oxides adsorb the intermediate species most easily when B = Nb, Mo, Ta, W, and Os, and their overpotentials are low when B = Mn, Fe, Co, Ru, Rh, and Ir. This work demonstrated that the machine learning methods based on SCE features can be applied to describe surface chemical reactions and accelerate the screening for potential perovskite catalysts with target properties.