Smart design of Rh-based hydrogen evolution electrocatalysts: integrating DFT, machine learning, and structural optimization for sustainable hydrogen energy

Hydrogen energy is vital for achieving carbon neutrality, with green hydrogen from water electrolysis being key. The hydrogen evolution reaction (HER) critically determines system viability, driving the search for high-performance, stable and affordable electrocatalysts. Rhodium (Rh)-based catalysts are promising platinum alternatives due to near-ideal hydrogen adsorption energy, tunable electronic structure, and stable activity across all pH ranges. This review highlights recent advances in Rh-based HER catalysts, including mechanisms, descriptors, materials, and optimization strategies. Density functional theory (DFT) indicates that Rh catalysts typically follow the Volmer-Heyrovsky-Tafel pathway, with performance governed by surface geometry and electronic states. Key activity descriptors are summarized, while combining DFT with machine learning enables high-throughput screening and rational catalyst design. Experimentally, activity and stability are improved through atomic-scale modulation, interface engineering, and carrier synergy. Rh-based catalysts are categorized into single atoms, nanoclusters, 2D metallenes, nanoparticles, and compounds (phosphides, sulfides, oxides, nitrides), with synthesis methods and performance characteristics reviewed. Remaining challenges include reducing synthesis cost, ensuring long-term durability, and achieving scalable production. Future research should deepen structure-activity understanding and integrate artificial intelligence to accelerate development of practical Rh-based HER catalysts.