Evaluation of Machine Learning Interatomic Potentials for the Properties of Gold Nanoparticles
Marco Fronzi, Roger D. Amos, Rika Kobayashi, Naoki Matsumura, K. Watanabe, Rafael K. Morizawa
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.
Topics & Concepts
NanoclustersBenchmarkingInteratomic potentialAb initioNanoparticleWorkflowMaterials scienceMolecular dynamicsAb initio quantum chemistry methodsComputer scienceNanotechnologyChemical physicsComputational chemistryChemistryMoleculeDatabaseOrganic chemistryMarketingBusinessMachine Learning in Materials ScienceComputational Drug Discovery MethodsNanocluster Synthesis and Applications