On Machine Learning Force Fields for Metallic Nanoparticles

TL;DR

This review discusses how machine learning algorithms can generate accurate and fast force fields for metallic nanoparticles, enabling better sampling of their complex energy landscapes and catalytic properties.

Contribution

It provides a comprehensive overview of machine learning methods for force field generation and discusses training database selection and applications to metallic nanoparticles.

Findings

ML force fields achieve near ab-initio accuracy

They enable long-time scale simulations of nanoparticles

Recent studies show improved sampling of energy landscapes

Abstract

Machine learning algorithms have recently emerged as a tool to generate force fields which display accuracies approaching the ones of the ab-initio calculations they are trained on, but are much faster to compute. The enhanced computational speed of machine learning force fields results key for modelling metallic nanoparticles, as their fluxionality and multi-funneled energy landscape needs to be sampled over long time scales. In this review, we first formally introduce the most commonly used machine learning algorithms for force field generation, briefly outlining their structure and properties. We then address the core issue of training database selection, reporting methodologies both already used and yet unused in literature. We finally report and discuss the recent literature regarding machine learning force fields to sample the energy landscape and study the catalytic activity of metallic nanoparticles.