Data-driven simulation and characterisation of gold nanoparticle melting

Claudio Zeni (), Kevin Rossi, Theodore Pavloudis, Joseph Kioseoglou, Stefano de Gironcoli, Richard E. Palmer and Francesca Baletto
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Claudio Zeni: King’s College London
Kevin Rossi: King’s College London
Theodore Pavloudis: Swansea University
Joseph Kioseoglou: Aristotle University of Thessaloniki
Stefano de Gironcoli: International School for Advanced Studies
Richard E. Palmer: Swansea University
Francesca Baletto: King’s College London

Nature Communications, 2021, vol. 12, issue 1, 1-9

Abstract: 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.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26199-7

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DOI: 10.1038/s41467-021-26199-7

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