(169bx) Direct Simulation of Supported Ag Nanoparticles Via Machine Learning Interatomic Potentials

Supported nanoparticles have a wide range of applications (thermal-, electro-, and photocatalysis, sensors, etc.) but their in-situ characterization and computational modeling prove challenging. Current nanoparticle models typically use low-index periodic models with defect-free surface due to their lower computational cost compared with nanoparticle models. The recent development of machine learning interatomic potentials (MLIPs) can provide computationally viable approximations of density functional theory calculations while maintaining similar levels of accuracy in energies and forces. We show our nanoparticle models are both accurate and computationally viable by using equivariant MLIPs. To benchmark the validity of MLIPs for nanoparticle simulations, we build upon the two recent works of Campbell et al^[1,2] that give experimental understanding of the adhesion of Ag nanoparticles to Ni(111) supported graphene and their chemical potential as a function of particle size. In this work, we train MLIPs via an iterative committee training scheme to identify poorly predicted structures and improve the training set. Simulated Annealing (SA) and hybrid Monte Carlo Molecular Dynamics (MCMD) simulations are performed to explore chemically relevant geometries of nanoparticles at various temperatures to make comparison to Campbell’s results. We demonstrate structural deviations from the idealized Winterbottom construction due to the finite size effects at particle sizes below approximately Ag₈₀₀. The nanoparticles are then analyzed to provide insights into the distributions of structures that may be present. This work marks progress toward understanding the structural properties of supported nanoparticles under experimentally relevant conditions as we move from the nanocluster (diameter below 1 nm) to the nanoparticle size regime (diameter above 1 nm).

(1) Rumptz, J. R.; Mao, Z.; Campbell, C. T. Size-Dependent Adsorption and Adhesion Energetics of Ag Nanoparticles on Graphene Films on Ni(111) by Calorimetry. ACS Catalysis. 2022, 12, (5), 2888-2897.

(2) Zhao, K.; Auerbach, D. J.; Campbell, C. T., Calorimetric Energies and Chemical Potentials of Metal Atoms in Catalytic Nanoparticles on Oxide and Carbon Supports: Improved Size Dependencies and Adhesion Energies. ACS Catalysis, 2023, 13, (21), 13968-13981.