On machine learning force fields for metallic nanoparticles

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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.
Original languageEnglish
Article number1654919
Number of pages32
JournalAdvances in Physics: X
Issue number1
Early online date12 Sept 2019
Publication statusE-pub ahead of print - 12 Sept 2019


  • force fields
  • machine learning
  • nanocatalysis
  • Nanoparticles
  • nanoscience


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