Monte Carlo Simulations of Au38(SCH3)24 Nanocluster Using Distance-Based Machine Learning Methods.
Antti PihlajamäkiJoonas HämäläinenJoakim LinjaPaavo NieminenSami MalolaTommi KärkkäinenHannu HäkkinenPublished in: The journal of physical chemistry. A (2020)
We present an implementation of distance-based machine learning (ML) methods to create a realistic atomistic interaction potential to be used in Monte Carlo simulations of thermal dynamics of thiolate (SR) protected gold nanoclusters. The ML potential is trained for Au38(SR)24 by using previously published, density functional theory (DFT) based, molecular dynamics (MD) simulation data on two experimentally characterized structural isomers of the cluster and validated against independent DFT MD simulations. This method opens a door to efficient probing of the configuration space for further investigations of thermal-dependent electronic and optical properties of Au38(SR)24. Our ML implementation strategy allows for generalization and accuracy control of distance-based ML models for complex nanostructures having several chemical elements and interactions of varying strength.
Keyphrases
- density functional theory
- monte carlo
- molecular dynamics
- machine learning
- sensitive detection
- big data
- primary care
- reduced graphene oxide
- healthcare
- artificial intelligence
- molecular dynamics simulations
- quality improvement
- quantum dots
- systematic review
- electronic health record
- randomized controlled trial
- single molecule
- gold nanoparticles
- high intensity
- body composition
- silver nanoparticles