Monte Carlo Simulations of Au38(SCH3)24 Nanocluster Using Distance-Based Machine Learning Methods
Pihlajamäki, A., Hämäläinen, J., Linja, J., Nieminen, P., Malola, S., Kärkkäinen, T., & Häkkinen, H. (2020). Monte Carlo Simulations of Au38(SCH3)24 Nanocluster Using Distance-Based Machine Learning Methods. Journal of Physical Chemistry A, 124(23), 4827-4836. https://doi.org/10.1021/acs.jpca.0c01512
Published inJournal of Physical Chemistry A
© 2020 American Chemical Society
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 characterised 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 generalisation and accuracy control of distance-based ML models for complex nanostructures having several chemical elements and interactions of varying strength.
PublisherAmerican Chemical Society
Publication in research information system
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Related funder(s)Academy of Finland
Funding program(s)Academy Programme, AoF; Research profiles, AoF
Additional information about fundingThis work was supported by Academy of Finland through the AIPSE research program with grant 315549 to H.H. and 315550 to T.K., through the Universities Profiling Actions with grant 311877 to T.K., and through H.H.’s Academy Professorship. MC simulations were done at the FGCI node in the University of Jyväskylä (persistent identifier: urn:nbn:fi:researchinfras-2016072533) and the DFT MD simulations were run as part of a PRACE project 2018194723 at the Barcelona Supercomputing Centre. ...
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