Evaluation of Machine Learning Interatomic Potentials for Gold Nanoparticles—Transferability towards Bulk

Evaluation of Machine Learning Interatomic Potentials for Gold Nanoparticles—Transferability towards Bulk

We analyse the efficacy of machine learning (ML) interatomic potentials (IP) in modelling gold (Au) nanoparticles. We have explored the transferability of these ML models to larger systems and established simulation times and size thresholds necessary for accurate interatomic potentials. To achieve...

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Bibliographic Details
Main Authors: Marco Fronzi, Roger D. Amos, Rika Kobayashi
Format: Article
Language:English
Published: MDPI AG 2023-06-01
Series:Nanomaterials
Subjects:
machine learning potentials
gold nanoparticles
molecular dynamics
structures
heat capacities
Online Access:https://www.mdpi.com/2079-4991/13/12/1832

Internet

https://www.mdpi.com/2079-4991/13/12/1832

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