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

被引：7

作者：

Fronzi, Marco ^{[1
]}

Amos, Roger D. ^{[1
]}

Kobayashi, Rika ^{[2
]}

Matsumura, Naoki ^{[3
]}

Watanabe, Kenta ^{[3
]}

Morizawa, Rafael K. ^{[3
]}

机构：

[1] Univ Technol Sydney, Ultimo, NSW 2007, Australia

[2] Australian Natl Univ, Canberra, ACT 2601, Australia

[3] Fujitsu Ltd, Kawasaki, Kanagawa 2118588, Japan

来源：

NANOMATERIALS | 2022年 / 12卷 / 21期

基金：

澳大利亚研究理事会;

关键词：

machine learning potentials; gold clusters; molecular dynamics; structures; heat capacities; MOLECULAR-DYNAMICS; CLUSTERS; HEAT;

D O I：

10.3390/nano12213891

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

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-20 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.

引用

页数：11

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