Machine learning-accelerated computational screening of CrNiCu ternary alloy as superior cocatalyst for photocatalytic hydrogen evolution

The development of cost-effective noble-metal-free cocatalysts with exceptional hydrogen evolution reaction (HER) activity is critical for advancing scalable and sustainable photocatalytic hydrogen production. Although platinum (Pt) remains a benchmark HER catalyst, its scarcity and high cost stimulates the search for viable alternatives. In this work, a machine learning (ML)-accelerated strategy is presented to screen highly active ternary CrNiCu alloys. Combining with density functional theory calculations, XGBoost regression models were trained to predict hydrogen adsorption energies and water dissociation energy barriers on CrNiCu alloy surfaces. Consequently, the theoretical exchange current densities were predicted for all possible compositions of CrNiCu alloys, enabling the identification of alloy catalysts with composition of 10∼30 at.% Cr, 30–50 at.% Ni, and 20–60 at.% Cu that exhibits superior HER activity than Pt. Stability assessment of optimal ternary CrNiCu alloys further confirms their excellent resistance to element segregation and hydroxyl poisoning under operational conditions. This work not only identifies promising ternary CrNiCu alloys of non-noble HER catalysts but also establishes an efficient ML-accelerated computational framework for the discovery of durable high-activity alloys for renewable energy applications.