Evaluation of Machine Learning Interatomic Potentials for the Properties of Gold Nanoparticles.
Marco FronziRoger D AmosRika KobayashiNaoki MatsumuraKenta WatanabeRafael K MorizawaPublished in: Nanomaterials (Basel, Switzerland) (2022)
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.
Keyphrases
- gold nanoparticles
- machine learning
- molecular dynamics simulations
- reduced graphene oxide
- big data
- sensitive detection
- electronic health record
- deep learning
- artificial intelligence
- molecular docking
- quality improvement
- current status
- heat stress
- atomic force microscopy
- fluorescent probe
- climate change
- mass spectrometry