Machine Learning-Accelerated Discovery of Earth-Abundant Bimetallic Electrocatalysts for the Hydrogen Evolution Reaction.

ABSTRACT: Despite platinum's well-established catalytic activity for the hydrogen evolution reaction (HER), its limited supply and steep cost hinder large-scale adoption. Earth-abundant bimetallic alloys thus emerge as attractive substitutes, though their vast compositional and structural diversity makes exhaustive density functional theory (DFT) screening unfeasible. Here, we introduce a machine learning (ML)-DFT workflow for the discovery and prioritization of bimetallic HER catalysts. By integrating EquiformerV2 into the AdsorbML surrogate-DFT pipeline, we efficiently predict hydrogen adsorption energies on thousands of alloy surfaces. Sabatier-volcano filtering combined with targeted DFT validation yields a mean absolute error of 0.12 eV across the screened space. Two surface motifs stand out: (

SUBMITTER: Oguz IC 

PROVIDER: S-EPMC12645476 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature