  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.
