These are preliminary reports that have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information. For more information, please see our FAQs.
ML-Au38-chemRxiv.pdf (3.91 MB)

Monte Carlo Simulations of Au38(SCH3)24 Nanocluster Using Distance-Based Machine Learning Methods

submitted on 21.02.2020, 07:52 and posted on 21.02.2020, 12:10 by Antti Pihlajamaki, Joonas Hamalainen, Joakim Linja, Paavo Nieminen, Sami Malola, Tommi Karkkainen, Hannu Hakkinen

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.


Email Address of Submitting Author


University of Jyvaskyla



ORCID For Submitting Author


Declaration of Conflict of Interest

no conflict of interest