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.

机器学习算法最近已成为一种生成力场的工具，该力场显示的精度接近对其进行训练的从头算式的计算，但计算速度更快。机器学习力场的提高的计算速度是建模金属纳米粒子的关键，因为它们的通量和多通道能量格局需要长期采样。在这篇综述中，我们首先正式介绍最常用的机器学习算法来生成力场，并简要概述它们的结构和属性。然后，我们解决培训数据库选择的核心问题，即文献中已经使用但尚未使用的报告方法。