Screening of transition metal dual-atom catalysts for hydrogen evolution reaction based on high-throughput calculation and searching surrogate prediction model using simple features

Abstract

Dual-atom catalysts for hydrogen evolution reaction have received widespread attention, but precise screening and prediction of high-performance catalysts through simple methods remains a challenge. In this study, we perform high-throughput density functional theory (DFT) calculation and machine learning (ML) to screen and predict the transition metal dual-atom catalysts with N-doped graphene support (TMDACs) for acidic hydrogen evolution reaction (HER). The Fe_Zn and V_Fe DACs were proposed to be the most promising candidates for Pt-based catalyst toward acidic HER from 406 TMDACs, based on the characteristics of HER activity, formation, thermodynamic stability, abundance, environmental friendliness. The Fe_Zn and V_Fe DACs with excellent HER performance is due to the synergistic effect deriving from the interaction between H and dual metal atoms in TMDACs. By determining 6 different ML models with four kind of input features, we find the artificial neural networks (ANN) model can predict the HER performance of TMDACs most accurately only using simple input features, including one-hot-encoding of atomic number and Gibbs free energies of transition metal single-atom catalysts. This work not only proposed the potential TMDACs with high HER performance, but also verified that the ANN model can accurately predict the HER activity of diatomic catalysts with simple input features.