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 NN 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.