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

Using liquid organic hydrogen carriers for the trans-oceanic shipment of hydrogen requires selective and low-cost dehydrogenation catalysts. Machine learning methods can accelerate the discovery of these catalysts. The state-of-the-art machine learning methods are however limited by challenges associated with building predictive models for large cyclic intermediates that adsorb and react on low-symmetry active sites. Focusing on methyl cyclohexane dehydrogenation to toluene, an industrially relevant hydrogen carrier, we introduce a physics-inspired machine learning approach to accelerate the design of selective and cost-effective non-magnetic bimetallic nanoparticle catalysts. This model is integrated with a microkinetic model to identify promising catalysts. The model reveals that modifying Pt nanoclusters with IB, IIB, and post-transition elements like Cu and Sn increases dehydrogenation rates, reduces unselective reactions, and lowers Pt utilization, consistent with prior experiments. This work presents a scalable, and efficient framework for designing bimetallic catalysts for dehydrogenating hydrogen carriers.