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• W4308331393 startingPage "3891" @default.
• W4308331393 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 Au20 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." @default.
• W4308331393 created "2022-11-11" @default.
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• W4308331393 date "2022-11-03" @default.
• W4308331393 modified "2023-09-26" @default.
• W4308331393 title "Evaluation of Machine Learning Interatomic Potentials for the Properties of Gold Nanoparticles" @default.
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• W4308331393 doi "https://doi.org/10.3390/nano12213891" @default.
• W4308331393 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36364667" @default.
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