No Cover Image

Journal article 74 views 14 downloads

Data-driven simulation and characterisation of gold nanoparticle melting

Claudio Zeni, Kevin Rossi, Theodore Pavloudis, Joseph Kioseoglou, Stefano de Gironcoli, Richard Palmer Orcid Logo, Francesca Baletto

Nature Communications, Volume: 12, Issue: 1

Swansea University Author: Richard Palmer Orcid Logo

  • 57899.pdf

    PDF | Version of Record

    Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made

    Download (1.77MB)

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 interpretab...

Full description

Published in: Nature Communications
ISSN: 2041-1723
Published: Springer Science and Business Media LLC 2021
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa57899
Tags: Add Tag
No Tags, Be the first to tag this record!
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.
College: College of Engineering
Issue: 1