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ML-Au38-chemRxiv.pdf (3.91 MB)

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

preprint
submitted on 21.02.2020 and posted on 21.02.2020 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.

History

Email Address of Submitting Author

hannu.j.hakkinen@jyu.fi

Institution

University of Jyvaskyla

Country

Finland

ORCID For Submitting Author

0000-0002-8558-5436

Declaration of Conflict of Interest

no conflict of interest

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