Machine learning-accelerated transition state prediction for strain-engineered high-entropy alloy catalysts

The hydrogen evolution reaction represents a critical bottleneck in renewable energy conversion, with transition state (TS) identification being essential for rational catalyst design. While strain engineering offers powerful pathways to modulate catalytic activity in high-entropy alloys (HEAs), conventional density functional theory (DFT) calculations face prohibitive computational costs due to inherent atomic-level disorder and expanded configurational space. Here, we present a fine-tuned graph neural network EquiformerV2 (eqV2) framework that dramatically accelerates TS discovery in strain-engineered Ir-Pt-Rh-Pd-Ru HEA catalysts. Our lightweight 31-million-parameter model achieves pathway prediction from hours-scale DFT calculations to second-scale predictions while maintaining exceptional accuracy: mean absolute errors below 0.1 eV for reaction energies and structural predictions within 0.1 Å root mean square deviation for 88.8% of configurations across Volmer, Heyrovsky, and Tafel pathways under biaxial strain. This methodology establishes a scalable computational framework that overcomes traditional limitations in high-dimensional catalyst screening, offering a generalizable strategy for artificial intelligence-accelerated next-generation electrocatalyst discovery.