Going Beyond the d-band Center to Design Intermetallic Catalysts for Nitrogen Reduction: A High-Throughput DFT and Machine Learning Study

This study combines high-throughput density functional theory (DFT) calculations with machine learning (ML) to uncover the key descriptors governing the nitrogen reduction reaction in 47 intermetallic compounds (IMCs) composed of Co, Ni, Al, Zn, V, Fe, Cu, and Pt, and the adsorption energies of key