Data-driven simulation and characterisation of gold nanoparticle melting

TL;DR

This paper develops machine learning force fields for gold nanoparticles to accurately simulate their melting behavior, providing insights into phase change mechanisms and melting temperatures consistent with experiments.

Contribution

It introduces transferable, interpretable ML force fields for gold nanoparticles and employs them to study melting processes with novel characterization methods.

Findings

Melting initiates at the outer layers of nanoparticles.

Predicted melting temperatures agree with experimental data.

Unsupervised learning characterizes local atomic environments during melting.

Abstract

The simulation and analysis of the thermal stability of nanoparticles, a stepping stone towards their application in technological devices, require fast and accurate force fields, in conjunction with effective characterisation methods. In this work, we develop efficient, transferable, and interpretable machine learning force fields for gold nanoparticles based on data gathered from Density Functional Theory calculations. We use them to investigate the thermodynamic stability of gold nanoparticles of different sizes (1 to 6 nm), containing up to 6266 atoms, concerning a solid-liquid phase change through molecular dynamics simulations. We predict nanoparticle melting temperatures in good agreement with available experimental data. Furthermore, we characterize the solid-liquid phase change mechanism employing an unsupervised learning scheme to categorize local atomic environments. We thus provide a data-driven definition of liquid atomic arrangements in the inner and surface regions of a nanoparticle and employ it to show that melting initiates at the outer layers.