Recommended by

Indexed by

Data-driven simulation and characterisation of gold nanoparticles melting

Claudio Zeni1,2*, Kevin Rossi3, Theodore Pavloudis4,5, Joseph Kioseoglou4, Stefano de Gironcoli1, Richard E. Palmer5, Francesca Baletto2

1 International School for Advanced Studies, Via Bonomea, 265, 34136, Trieste, IT

2 Department of Physics, King's College London, London, WC2R 2LS, UK

3 Laboratory of Nanochemistry, Institute of Chemistry and Chemical Engineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, CH

4 Department of Physics, Aristotle University of Thessaloniki, Thessaloniki GR-54124, GR

5 College of Engineering, Swansea University, Bay Campus, Fabian Way, Swansea, SA1 8EB, UK

* Corresponding authors emails: czeni@sissa.it
DOI10.24435/materialscloud:s0-24 [version v1]

Publication date: Aug 11, 2021

How to cite this record

Claudio Zeni, Kevin Rossi, Theodore Pavloudis, Joseph Kioseoglou, Stefano de Gironcoli, Richard E. Palmer, Francesca Baletto, Data-driven simulation and characterisation of gold nanoparticles melting, Materials Cloud Archive 2021.131 (2021), doi: 10.24435/materialscloud:s0-24.


We develop efficient, accurate, transferable, and interpretable machine learning force fields for Au nanoparticles, based on data gathered from Density Functional Theory calculations. We then use them to investigate the thermodynamic stability of Au nanoparticles of different sizes (1 to 6 nm), containing up to 6266 atoms, with respect to a solid-liquid phase change through molecular dynamics simulations. We predict nanoparticle melting temperatures in good agreement with respect to available experimental data. Furthermore, we characterize in detail the solid to liquid phase change mechanism employing an unsupervised learning scheme to categorize local atomic environments. We thus provide a rigorous and 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. The record contains all the MD simulation trajectories carried out using mapped machine learning force fields in LAMMPS, the machine learning force fields as LAMMPS pair potentials, and the ab initio training data used to construct such force fields.

Materials Cloud sections using this data

No Explore or Discover sections associated with this archive record.


File name Size Description
3.9 GiB Melting MD simulations of Au nanoparticles with 147, 309, 561, 923, 2869 and 6266 atoms. Contains also the LAMMPS FLARE machine learning force fields used to simulate them.
1.1 MiB Data used to train the LDA and r-PBE ML-FFs in FLARE .json format
2.5 KiB README file containing detailed information about the data present in the archives


Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Preprint (Paper in which the method used to generate the force field, the MD simulation protocol, the data and the results emerging from the data are discussed.)


Gold Nanoparticle molecular dynamics simulation machine learning H2020 MaX ERC

Version history:

2021.131 (version v1) [This version] Aug 11, 2021 DOI10.24435/materialscloud:s0-24