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

6533b829fe1ef96bd128ab15

RESEARCH PRODUCT

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

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

subject

010304 chemical physics business.industry Chemistry Monte Carlo method Thermal dynamics 010402 general chemistry Machine learning computer.software_genre 01 natural sciences 0104 chemical sciences Interaction potential 0103 physical sciences Cluster (physics)Artificial intelligence Physical and Theoretical Chemistry business computer Distance based

description

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 thiol...

year	journal	country	edition	language
2020-05-15	The Journal of Physical Chemistry A

https://doi.org/10.1021/acs.jpca.0c01512

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