Rational Designing of Bimetallic/Trimetallic Hydrogen Evolution Reaction Catalysts Using Supervised Machine Learning

Abstract

Cost-efficient electrocatalysts to replace precious platinum group metals- (PGMs-) based catalysts for the hydrogen evolution reaction (HER) carry significant potential for sustainable energy solutions. Machine learning (ML) methods have provided new avenues for intelligent screening and predicting efficient heterogeneous catalysts in recent years. We coalesce density functional theory (DFT) and supervised ML methods to discover earth-abundant active heterogeneous NiCoCu-based HER catalysts. An intuitive generalized microstructure model was designed to study the adsorbate's surface coverage and generate input features for the ML process. The study utilizes optimized eXtreme Gradient Boost Regression (XGBR) models to screen NiCoCu alloy-based catalysts for HER. We show that the most active HER catalysts can be screened from an extensive set of catalysts with this approach. Therefore, our approach can provide an efficient way to discover novel heterogeneous catalysts for various electrochemical reactions.