Journal article 878 views 122 downloads
Data-driven simulation and characterisation of gold nanoparticle melting
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Francesca Baletto
Nature Communications, Volume: 12, Issue: 1
Swansea University Author:
Richard Palmer
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DOI (Published version): 10.1038/s41467-021-26199-7
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...
| Published in: | Nature Communications |
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| ISSN: | 2041-1723 |
| Published: |
Springer Science and Business Media LLC
2021
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa57899 |
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| 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. |
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| College: |
Faculty of Science and Engineering |
| Funders: |
C.Z. acknowledges funding by the Engineering and Physical Sciences Research Council (EPSRC) through the Centre for Doctoral Training Cross-Disciplinary Approaches to Non-Equilibrium Systems (CANES, Grant No. EP/L015854/1) and by the European Union’s Horizon 2020 research and innovation programme (Grant No. 824143, MaX ‘MAterials design at the eXascale’ Centre of Excellence). K.R. has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Marie Curie Individual Fellowship Grant Agreement No. 890414). S.d.G. acknowledges funding from the European Union’s Horizon 2020 research and innovation programme (Grant No. 824143, MaX MAterials design at the eXascale Centre of Excellence). F.B. acknowledges the financial support offered by the Royal Society under project number RG120207 and DIPC for supporting her visiting professorship. We are grateful to the UK Materials and Molecular Modelling Hub for computational resources, partially funded by EPSRC (EP/P020194/1 and EP/T022213/1), our membership of the Materials Chemistry Consortium, funded by EPSRC (EP/R029431), the Swiss National Supercomputer Centre (CSCS) (project ‘sm54’) and to the Supercomputing Wales project, partially funded by the European Regional Development Fund (ERDF) via the Welsh Government. |
| Issue: |
1 |