Machine-learning-assisted high-throughput screening for accelerating the discovery of diatomic catalysts in N₂ electroreduction

Abstract

Ambient-condition nitrogen reduction reactions(NRR) provides a sustainable pathway for green ammonia synthesis. Bimetallic catalysts supported on nitrogendoped graphene-like (MAMB-C2N) have garnered significant research interest owing to their remarkable catalytic activity and structural stability. However, conventional experimental techniques and density functional theory (DFT) calculations struggle to systematically evaluate its catalytic activity across all possible coordination environments. This study integrates DFT with six distinct machine learning (ML) methods, delivering accurate and efficient Gibbs free energy predictions for all three reaction pathways -distal, alternating, and enzymatic. Twenty-seven transition metal dimers were considered, comprehensive evaluation of catalytic activity across 729 MAMB-C2N configurations. The identification of 106 highly active catalysts was achieved through a three-stage screening approach ， followed by construction of structure-activity relationships between descriptors and ΔG using the SISSO algorithm. This study establish an important framework for designing sophisticated reaction processes and systematically screening highly active bimetallic NRR electrocatalysts.Keyword：Nitrogen reduction reaction (NRR)；Dual-atom catalysts (DACs)；SISSO algorithm；DFT-ML integration；Comprehensive catalyst screening