Carbon-free electrocatalytic nitrogen reduction reaction (NRR) offers an environmentally sustainable alternative to the current Haber-Bosch process in the industry. However, this process is still limited by the scaling relations and the competitive hydrogen evolution reaction (HER). Using the density functional theory, we theoretically present a strategy for separating the active sites of the N 2 activation and the hydrogenation of NH z (z = 1, 2) intermediates on the Ni 3 Mo surface, which subtly optimized the adsorption of intermediates and bypasses the scaling relations, achieving efficient NRR with an ultralow limiting potential of − 0.19 V. Besides, the Ni 3 Mo greatly protects the active centers of NRR from competitive H adsorption and retard the undesired HER, enabling highly selective NH 3 synthesis. The above theoretical designs are supported by proof-of-concept experimental results, where Ni 3 Mo exhibits excellent NRR performance with the NH 3 yield rate of 17.35 ± 0.3 μg h −1 cm −2 at − 0.35 V.
The hydrogen evolution reaction (HER) in alkaline media is a promising strategy for sustainable hydrogen production, but the exploration of efficient and durable HER electrocatalysts is often hindered by the empirical and time-consuming nature of traditional synthesis. Herein, a machine learning (ML)–driven strategy combining Bayesian optimization is introduced to achieve the rational design of Ni 3 S 4 /Ni 3 Mo heterostructures for alkaline HER. By coupling predictive modeling with experimental feedback, this approach efficiently navigated a complex synthesis space and identified conditions yielding a structurally and electronically optimized catalyst. The optimized Ni 3 S 4 /Ni 3 Mo exhibits a vibrant morphological evolution—from compact buds to blooming petal-like structures—enabling enriched active sites and accelerated mass transport. Guided by Bayesian optimization, the optimized Ni 3 S 4 /Ni 3 Mo achieves a 10.5-fold enhancement in C dl and delivers an exceptionally low overpotential of 18.2 mV at 100 mA cm −2 , outperforming most reported transition-metal catalysts and even surpassing commercial Pt/C. Mechanistic insights from in situ Raman and DFT reveal that interfacial charge redistribution between Ni 3 S 4 and Ni 3 Mo optimizes H* adsorption (Δ G H* ≈ 0.04 eV) and significantly reduces the water-dissociation barrier (0.08 eV), thereby accelerating reaction kinetics. This work demonstrates how the ML-guided optimization can synergistically couple morphology control, interfacial engineering with electronic tuning, offering a generalizable framework for intelligent catalyst discovery and mechanistic understanding in electrochemical energy conversion.
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