@article{f405776c19184e3cae4d0966bbd9da67,
title = "Evaluation of Machine Learning Interatomic Potentials for the Properties of Gold Nanoparticles",
abstract = "We have investigated Machine Learning Interatomic Potentials in application to the properties of gold nanoparticles through the DeePMD package, using data generated with the ab-initio VASP program. Benchmarking was carried out on Au (Formula presented.) nanoclusters against ab-initio molecular dynamics simulations and show we can achieve similar accuracy with the machine learned potential at far reduced cost using LAMMPS. We have been able to reproduce structures and heat capacities of several isomeric forms. Comparison of our workflow with similar ML-IP studies is discussed and has identified areas for future improvement.",
keywords = "gold clusters, heat capacities, machine learning potentials, molecular dynamics, structures",
author = "Marco Fronzi and Amos, {Roger D.} and Rika Kobayashi and Naoki Matsumura and Kenta Watanabe and Morizawa, {Rafael K.}",
note = "Funding Information: The authors would like to thank Mike Ford, Tim Duignan and David Reith for helpful discussions. The authors gratefully acknowledge the financial support of Australian Government through the Australian Research Council (ARC DP200101217). The theoretical calculations in this research were undertaken with the assistance of resources from the National Computational Infrastructure (NCI), which is supported by the Australian Government. The theoretical calculations in this work were also supported by resources provided by the Pawsey Supercomputing Centre. Publisher Copyright: {\textcopyright} 2022 by the authors.",
year = "2022",
month = nov,
doi = "10.3390/nano12213891",
language = "English",
volume = "12",
journal = "Nanomaterials",
issn = "2079-4991",
publisher = "Multidisciplinary Digital Publishing Institute (MDPI)",
number = "21",
}