Machine Learning-Guided Discovery of PGM-Lean High-Entropy Alloys for Efficient Solar Hydrogen Evolution

Abstract

High-entropy alloys (HEAs) have emerged as a powerful materials platform for electrocatalysis due to their tunable surface energetics, structural stability, and diverse local atomic environments arising from multielement interactions. Composed of several principal elements in near-equimolar ratios, HEAs leverage high configurational entropy to stabilize single-phase solid solutions, while surface heterogeneity creates new catalytic motifs in which collective electronic and geometric effects yield activities exceeding those of pure metals. These attributes make HEAs particularly promising for the hydrogen evolution reaction (HER). Platinum-group-metal-containing HEAs (PGM-HEAs) exhibit exceptional HER activity and durability in acidic environments; however, their reliance on multiple precious metals limits large-scale deployment. Recent efforts demonstrate that partial substitution with earth-abundant transition metals can significantly reduce noble-metal content without sacrificing performance. Despite this progress, the atomic-scale origins of HER activity in HEAs—specifically the geometric and electronic descriptors governing optimal hydrogen binding—remain insufficiently understood. Here, we present an integrated density functional theory (DFT) and machine learning (ML) framework for discovering PGM-lean HEAs optimized for HER. Equimolar binary and ternary alloys are constructed from a nine-metal design space (Pt, Pd, Ir, Rh, Fe, Co, Ni, Mo, W), yielding 120 unique compositions. Representative surface configurations are selected using Kennard–Stone sampling and modeled as close-packed FCC(111) and BCC(110) slabs. Hydrogen adsorption energetics, and local active-site descriptors are computed using DFT and analyzed using ML models to establish structure–property relationships. Benchmark calculations on elemental FCC(111) surfaces reproduce expected periodic trends, with hydrogen adsorption free energies ranging from −0.46 eV on Ni(111) to −0.24 eV on Pt(111) at 1/4 monolayer coverage, consistent with d-band theory. Coverage effects are quantified by comparing 2×2×7 and 3×3×7 slab models, revealing stabilization of H* by approximately 0.02 eV at lower coverage. These results validate the computational methodology and establish a robust foundation for screening multicomponent HEA surfaces. The combined DFT–ML framework enables the rational identification of cost-effective, high-performance HEA electrocatalysts while providing fundamental insight into active-site chemistry in complex alloy systems. All computational data will be made available upon request to promote transparency and reproducibility.