Dynamic Kinetic Evaluation Favors Compositionally Diverse Multicomponent Alloy Nanoparticles for Hydrogen Evolution
Abstract
High-entropy alloy (HEA) nanoparticles offer chemically diverse catalytic sites, but identifying optimal compositions across their large design space remains challenging. We developed a framework combining machine-learning energy prediction, thermodynamic Monte Carlo sampling, Bayesian optimization, and kinetic Monte Carlo (kMC) simulations to screen 85-atom nanoparticle models across a nine-element composition space for the hydrogen evolution reaction. Static optimization based on adsorption energies selected Pt-rich, low-component compositions, whereas kMC-based optimization ranked the multielement composition Au33Co20Cu6Pd26 highest among the evaluated candidates. Cycle-resolved analysis further showed hydrogen adsorption at Co/Pd-rich environments, migration through intermediate-binding sites, and frequent Heyrovsky desorption at weaker-binding, Au-containing environments. This pathway-level evidence supports a multisite reaction picture and illustrates how incorporating kinetics can complement static adsorption-energy screening.
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BibTeX
@misc{otsuka2026dynamic,
title = {{Dynamic Kinetic Evaluation Favors Compositionally Diverse Multicomponent Alloy Nanoparticles for Hydrogen Evolution}},
author = {Koki Otsuka and Anh Khoa Augustin Lu and Koji Shimizu and Satoshi Watanabe},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.08040},
eprint = {2610.08040},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.08040},
}
RIS
TY - UNPB
TI - Dynamic Kinetic Evaluation Favors Compositionally Diverse Multicomponent Alloy Nanoparticles for Hydrogen Evolution
AU - Koki Otsuka
AU - Anh Khoa Augustin Lu
AU - Koji Shimizu
AU - Satoshi Watanabe
PY - 2026
DA - 2026/10/06
PB - arXiv
DO - 10.48550/arxiv.2610.08040
UR - https://arxiv.org/abs/2610.08040
AB - High-entropy alloy (HEA) nanoparticles offer chemically diverse catalytic sites, but identifying optimal compositions across their large design space remains challenging. We developed a framework combining machine-learning energy prediction, thermodynamic Monte Carlo sampling, Bayesian optimization, and kinetic Monte Carlo (kMC) simulations to screen 85-atom nanoparticle models across a nine-element composition space for the hydrogen evolution reaction. Static optimization based on adsorption energies selected Pt-rich, low-component compositions, whereas kMC-based optimization ranked the multielement composition Au_33Co_20Cu_6Pd_26 highest among the evaluated candidates. Cycle-resolved analysis further showed hydrogen adsorption at Co/Pd-rich environments, migration through intermediate-binding sites, and frequent Heyrovsky desorption at weaker-binding, Au-containing environments. This pathway-level evidence supports a multisite reaction picture and illustrates how incorporating kinetics can complement static adsorption-energy screening.
ER -