Evaluation of Machine Learning Interatomic Potentials for the Properties of Gold Nanoparticles

Marco Fronzi, Roger D. Amos, Rika Kobayashi, Naoki Matsumura, Kenta Watanabe, Rafael K. Morizawa

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number3891
JournalNanomaterials
Volume12
Issue number21
DOIs
Publication statusPublished - 2022 Nov

Keywords

  • gold clusters
  • heat capacities
  • machine learning potentials
  • molecular dynamics
  • structures

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Materials Science(all)

Fingerprint

Dive into the research topics of 'Evaluation of Machine Learning Interatomic Potentials for the Properties of Gold Nanoparticles'. Together they form a unique fingerprint.

Cite this