Bridging Nanostructure Growth and Gas-Sensing Kinetics in Metal-Functionalized Graphene Using Machine Learning Molecular Dynamics

Graphene functionalized with catalytic transition metals offers high-performance chemiresistive gas sensing by coupling graphene’s exceptional electronic transport with the metal’s catalytic activity; yet the atomistic relationships connecting synthesis parameters, morphological outcomes, and sensor gas–surface reaction kinetics remain elusive. We developed an equivariant machine-learning interatomic potential with DFT accuracy to enable high-fidelity molecular dynamics (MD) simulations, from metal nanostructure growth on graphene to device-level sensing kinetics. Demonstrating our approach, we investigate Pt-functionalized graphene for H₂ detection. MD simulations validated by TEM show that Pt deposition begins with dispersed nuclei coalescing into polycrystalline nanoclusters, while both MD and Raman spectroscopy reveal predominantly non-covalent metal–graphene interactions that induce moderate local strain and doping, while preserving graphene’s structural integrity. MD simulations confirm H₂ dissociative chemisorption and recombinative desorption primarily on Pt nanoclusters, with negligible spillover or chemical interaction with pristine graphene. However, H adsorption on Pt attenuates the Pt–graphene interfacial binding, providing an indirect pathway for gas sensing. Adsorption/desorption kinetics reveal that an intermediate loading of metal nanostructures minimizes the detection limit; lower loadings facilitate faster response/recovery kinetics and enhance signal transduction, whereas higher loadings increase interfacial binding and graphene doping. The developed framework accurately models metallic nanostructure growth on graphene, elucidates the gas-sensing mechanism, and links gas–surface kinetics to nanostructure morphology, establishing a multiscale predictive pipeline from synthesis conditions to gas-sensor kinetics.