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

Antti Pihlajamäki; Joonas Hämäläinen; Joakim Linja; Paavo Nieminen; Sami Malola; Tommi Kärkkäinen; Hannu Häkkinen

doi:10.1021/acs.jpca.0c01512

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

J Phys Chem A. 2020 Jun 11;124(23):4827-4836. doi: 10.1021/acs.jpca.0c01512. Epub 2020 May 29.

Authors

Antti Pihlajamäki¹, Joonas Hämäläinen², Joakim Linja², Paavo Nieminen², Sami Malola¹, Tommi Kärkkäinen², Hannu Häkkinen^{1

3}

Affiliations

¹ Department of Physics, Nanoscience Center, University of Jyväskylä, FI-40014 Jyväskylä, Finland.
² Faculty of Information Technology, University of Jyväskylä, FI-40014 Jyväskylä, Finland.
³ Department of Chemistry, Nanoscience Center, University of Jyväskylä, FI-40014 Jyväskylä, Finland.

PMID: 32412747
DOI: 10.1021/acs.jpca.0c01512

Abstract

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.