High-throughput DFT screening of bimetallic alloys for selective ammonia synthesis via electrocatalytic N2 activation

Abstract

Ammonia, a cornerstone of modern agriculture and a promising carbon-free energy carrier, is conventionally synthesized via the energy-intensive Haber–Bosch process. In pursuit of sustainable alternatives, electrocatalytic nitrogen reduction reaction (NRR) has garnered significant attention. However, the inert NN triple bond and the competing hydrogen evolution reaction (HER) pose formidable challenges. This study pioneers an integrated computational approach, combining high-throughput density functional theory (DFT), machine learning, and ab initio thermodynamics, to identify and rationalize high-performance bimetallic NRR catalysts. Among 20 screened alloys, CoRu emerges as a Pareto-optimal catalyst, demonstrating exceptional activity, selectivity, and stability. Ru's unique electronic modulation, manifested through orbital-selective hybridization and interfacial dipole fields, decouples the traditional trade-offs between NRR activity and HER suppression. Mechanistic insights reveal that CoRu facilitates moderate N₂ adsorption and a record-low overpotential of 0.28 V, while suppressing HER to achieve a faradaic efficiency of 72%. Furthermore, machine learning models trained on DFT-derived descriptors enable inverse design of novel alloys, predicting NiRu as a high-potential candidate. This study not only decodes the electronic origins of bimetallic synergy but also provides a blueprint for accelerating the discovery of next-generation electrocatalysts, heralding a transformative strategy to replace energy-intensive Haber–Bosch processes.