Constructing machine learning potential for metal nanoparticles of varying sizes via basin-hoping Monte Carlo and active learning

Nanoparticles,distinguished by their unique chemical and physical properties,have emerged as focal points within the realm of materials science.Traditional theoretical approaches for atomic simulations mainly include empirical force field and ab initio simulations,with the former offering efficiency but limited reliability,and the latter providing accuracy but restricted to systems of relatively small sizes.Herein,we propose a systematic strategy and automated workflow designed for collecting a diverse types of atomic local environments within a training dataset.This includes small nanoclusters,nanoparticles,as well as surface and bulk systems with periodic boundary conditions.The objective is to construct a machine learning potential tailored for pure metal nanoparticle simulations of varying sizes.Through rigorous validation,we have shown that our trained machine learning potential is capable of effectively driving molecular dynamics simulations of nanoparticles across a wide temperature range,especially within the nanoscale regime.Remarkably,this is achieved while preserving the accuracy typically associated with ab initio methods.