Nanoparticle Heterogeneous Catalysis Dynamics Simulations with Machine Learned Force Fields

Quantitative understanding and control of interfacial reactions between the gas-phase and solid surfaces are crucial for improving numerous catalysis and energy conversion systems. Examples of these interfacial phenomena include H₂ splitting and CO adsorption on nanoparticles, both of which are important industrial processes and lead to markedly different particle behaviors. These disparate particle responses cannot be resolved under the spatial and temporal resolutions of current experimental techniques, lending this problem to be solved by molecular dynamics simulations. We train a collection of robust, Bayesian machine learned force fields (MLFFs) using the FLARE on-the-fly active learning framework implemented using Gaussian Process regression. Molecular dynamics (MD) simulations are used to sample atomic configurations and density functional theory is only called upon when the Bayesian uncertainty exceeds a threshold. This workflow yields both acceleration in time-to-solution and an increase in computational efficiency relative to ab initio MD. The resulting MLFFs retain first principles accuracy, are fast, and are uncertainty-aware. Following a rigorous validation scheme, through comparison of the dynamic evolution of these particles and bulk systems to available x-ray data (i.e., extended x-ray absorption fine spectra) and static benchmarks, long timescale MD simulations for freestanding metal nanoparticle systems (e.g., Pt, Au, PdAu, and CuPt) are performed. In addition to the bare particles, reaction mechanisms under gaseous exposure (e.g., H₂ and CO) are also investigated. These MLFFs allow for the simultaneous study of atomistic mechanisms occurring on these nanoparticles under exposure to reactive atmospheres and the evolution of their structural morphologies.