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<p>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.
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Machine Learning for Memory Reduction in the Implicit Monte Carlo Simulations of Thermal Radiative Transfer
