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.

Data-driven simulation and characterisation of gold nanoparticle melting / Zeni, C.; Rossi, K.; Pavloudis, T.; Kioseoglou, J.; de Gironcoli, S.; Palmer, R. E.; Baletto, F.. - In: NATURE COMMUNICATIONS. - ISSN 2041-1723. - 12:1(2021), pp. 1-9. [10.1038/s41467-021-26199-7]

Data-driven simulation and characterisation of gold nanoparticle melting

Zeni C.
Investigation
;
de Gironcoli S.
Membro del Collaboration group
;
2021-01-01

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.
2021
12
1
1
9
6056
10.1038/s41467-021-26199-7
https://arxiv.org/abs/2107.00330
Zeni, C.; Rossi, K.; Pavloudis, T.; Kioseoglou, J.; de Gironcoli, S.; Palmer, R. E.; Baletto, F.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11767/126471
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