CUED Publications database

Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials

John, ST and Csányi, G (2017) Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials. Journal of Physical Chemistry B, 121. pp. 10934-10949. ISSN 1520-6106

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© 2017 American Chemical Society. We introduce a computational framework that is able to describe general many-body coarse-grained (CG) interactions of molecules and use it to model the free energy surface of molecular liquids as a cluster expansion in terms of monomer, dimer, and trimer terms. The contributions to the free energy due to these terms are inferred from all-atom molecular dynamics (MD) data using Gaussian Approximation Potentials, a type of machine-learning model that employs Gaussian process regression. The resulting CG model is much more accurate than those possible using pair potentials. Though slower than the latter, our model can still be faster than all-atom simulations for solvent-free CG models commonly used in biomolecular simulations.

Item Type: Article
Divisions: Div C > Applied Mechanics
Depositing User: Cron Job
Date Deposited: 14 Nov 2017 01:52
Last Modified: 04 Mar 2021 03:54
DOI: 10.1021/acs.jpcb.7b09636