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, elementary processes and lead to markedly different particle behaviors. This work demonstrates a collection of robust Bayesian machine learned force fields (MLFFs) trained with an on-the-fly active learning framework implemented using Gaussian Process regression in the FLARE code. Bayesian active-learning is paired with molecular dynamics (MD) simulations to sample atomic configurations and density functional theory is only called upon when the uncertainty of an atomic environment 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 adsorption fine spectra) and static theoretical benchmarks, long timescale MD simulations for freestanding metal nanoparticle systems (e.g., Pt, Au, PdAu, and CuPt) are performed. Monte carlo MD simulations are also performed to explore the lowest energy configurations of the bimetallic particles. In addition to the bare particles, reaction mechanisms under gaseous exposure (e.g., H2 and CO) are also investigated. In addition, morphological evolution of these particles under thermal and gaseous exposures are also observed. Ultimately, 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 at quantum-mechanical accuracy.

Machine-learned force-fields for the thermal and adsorbate-induced dynamic structural evolution of mono- and bi-metallic nanoparticles

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