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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

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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 interpretab...

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Published in: Nature Communications
ISSN: 2041-1723
Published: Springer Science and Business Media LLC 2021
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spelling 2022-07-07T13:16:08.2910677 v2 57899 2021-09-15 Data-driven simulation and characterisation of gold nanoparticle melting 6ae369618efc7424d9774377536ea519 0000-0001-8728-8083 Richard Palmer Richard Palmer true false 2021-09-15 MECH 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. Journal Article Nature Communications 12 1 Springer Science and Business Media LLC 2041-1723 1 12 2021 2021-12-01 10.1038/s41467-021-26199-7 COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University Another institution paid the OA fee 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. 2022-07-07T13:16:08.2910677 2021-09-15T15:27:41.2013666 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Claudio Zeni 1 Kevin Rossi 2 Theodore Pavloudis 3 Joseph Kioseoglou 4 Stefano de Gironcoli 5 Richard Palmer 0000-0001-8728-8083 6 Francesca Baletto 7 57899__21581__fe3169a67a764b67a8226e17ecc008f7.pdf 57899.pdf 2021-11-18T15:56:01.0798794 Output 1858941 application/pdf Version of Record true 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 true eng http://creativecommons.org/licenses/by/4.0/
title Data-driven simulation and characterisation of gold nanoparticle melting
spellingShingle Data-driven simulation and characterisation of gold nanoparticle melting
Richard Palmer
title_short Data-driven simulation and characterisation of gold nanoparticle melting
title_full Data-driven simulation and characterisation of gold nanoparticle melting
title_fullStr Data-driven simulation and characterisation of gold nanoparticle melting
title_full_unstemmed Data-driven simulation and characterisation of gold nanoparticle melting
title_sort Data-driven simulation and characterisation of gold nanoparticle melting
author_id_str_mv 6ae369618efc7424d9774377536ea519
author_id_fullname_str_mv 6ae369618efc7424d9774377536ea519_***_Richard Palmer
author Richard Palmer
author2 Claudio Zeni
Kevin Rossi
Theodore Pavloudis
Joseph Kioseoglou
Stefano de Gironcoli
Richard Palmer
Francesca Baletto
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container_title Nature Communications
container_volume 12
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publishDate 2021
institution Swansea University
issn 2041-1723
doi_str_mv 10.1038/s41467-021-26199-7
publisher Springer Science and Business Media LLC
college_str Faculty of Science and Engineering
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hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
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description 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.
published_date 2021-12-01T04:09:28Z
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