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 DFT calculations with machine learning techniques to uncover the key descriptors governing the nitrogen reduction reaction (NRR) in intermetallic compounds (IMCs). A dataset of 47 bimetallic IMCs was constructed, and the adsorption energies of key intermediates (N2, N2H, and NH3) were systematically evaluated across all accessible surface sites, yielding approximately 1,200 data points. By incorporating intrinsic material properties along with electronic descriptors including s-, p- and d-band centers and fillings, as well as Bader charges of atoms neighboring the adsorbate, predictive ML models were developed with mean absolute errors of 0.26 eV for N2, 0.39 eV for N2H, and 0.17 eV for NH3 adsorption. Importantly, accurate predictions are obtained with only 20 key features, enabling the use of simple and computationally efficient ML models. SHAP analysis indicates that p- and s-band characteristics play a more prominent role in determining adsorption strength than the traditionally used d-band center, particularly for N2 and N2H intermediates. Beyond their established importance in systems containing p-block elements or nearly filled d-band metals, s- and p-orbitals are also found to contribute significantly to transition-metal alloys activity such as Fe-Co, driven by adsorption-induced sp-d hybridization. By challenging the d-band-centric paradigm and identifying s- and p-band descriptors as critical yet overlooked contributors, this work redefines the electronic descriptor space for intermetallic NRR catalysts and lays the groundwork for DFT-ML-guided discovery of non-noble materials for sustainable ammonia synthesis.