Data-driven simulation and characterisation of gold nanoparticle melting

Efficient theoretical methods for the structural analysis of nanoparticles are very much needed. Here the authors demonstrate the use of machine-learning force fields and of a data-driven approach to study the thermodynamical stability and elucidate the melting process of gold nanoparticles.

Gold nanoparticles’ unique properties are determined by their structure, which, in turn, shape-shifts as a function of temperature. We adopt data-driven algorithms to model and characterize these changes in an efficient, accurate, and automated fashion.

Authors: 
Claudio Zeni, Kevin Rossi, Theodore Pavloudis, Joseph Kioseoglou, Stefano de Gironcoli, Richard E. Palmer & Francesca Baletto 
Featured in: https://www.nature.com/articles/s41467-021-26199-7