Harnessing Physics-inspired Machine Learning to Design Nanocluster Catalysts for Dehydrogenating Liquid Organic Hydrogen Carriers

Chuhong Lin, Bryan C. S. Lee, Uzma Anjum, Asmee Prabhu, Neeru Chaudhary, Rong Xu, Tej S Choksi

Keywords: Machine learning, Active learning, bimetallic nanoparticles, hydrogen carriers

TL;DR: Using physics-inspired machine learning we present a new paradigm to design catalysts for reactions involving large molecules on low symmetry active sites, thus overcoming a level of complexity that limits state-of-the-art foundational models.

Confirmation Of Submission Requirements: I submit a previously published paper. It was published in an archival peer–reviewed venue on or after September 8th 2024, I specify the DOI in the field below, and I submit the camera-ready version of the paper.

DOI: https://doi.org/10.1016/j.apcatb.2025.125192

Submission Number: 143