A1 Journal article (refereed)
Monte Carlo Simulations of Au38(SCH3)24 Nanocluster Using Distance-Based Machine Learning Methods (2020)


Pihlajamäki, Antti; Hämäläinen, Joonas; Linja, Joakim; Nieminen, Paavo; Malola, Sami; Kärkkäinen, Tommi; Häkkinen, Hannu (2020). Monte Carlo Simulations of Au38(SCH3)24 Nanocluster Using Distance-Based Machine Learning Methods. Journal of Physical Chemistry A, 124 (23), 4827-4836. DOI: 10.1021/acs.jpca.0c01512


JYU authors or editors


Publication details

All authors or editors: Pihlajamäki, Antti; Hämäläinen, Joonas; Linja, Joakim; Nieminen, Paavo; Malola, Sami; Kärkkäinen, Tommi; Häkkinen, Hannu

Journal or series: Journal of Physical Chemistry A

ISSN: 1089-5639

eISSN: 1520-5215

Publication year: 2020

Volume: 124

Issue number: 23

Pages range: 4827-4836

Publisher: American Chemical Society

Publication country: United States

Publication language: English

DOI: http://doi.org/10.1021/acs.jpca.0c01512

Open Access: Publication channel is not openly available

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/69062


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 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.


Keywords: nanoparticles; Monte Carlo methods; simulation; machine learning


Contributing organizations


Related projects

Structure prediction of hybrid nanoparticles via artificial intelligence (HNP-AI)
Häkkinen, Hannu
Academy of Finland
01/01/2018-31/12/2021
STRUCTURE PREDICTION OF HYBRID NANOPARTICLES VIA ARTIFICIAL INTELLIGENCE
Kärkkäinen, Tommi
Academy of Finland
01/01/2018-31/12/2021


Ministry reporting: Yes

Reporting Year: 2020


Last updated on 2020-18-08 at 13:02