Literature DB >> 30129962

Understanding three-body contributions to coarse-grained force fields.

Christoph Scherer1, Denis Andrienko.   

Abstract

Coarse-graining is a systematic reduction of the number of degrees of freedom used to describe a system of interest. Coarse-graining can be thought of as a projection on the coarse-grained degrees of freedom and is therefore dependent on the number and type of basis functions used to represent the coarse-grained force field. We show that many-body extensions of the coarse-grained force field can result in substantial changes of the two-body interactions, making them much more attractive at short distances. This interplay can be alleviated by first parametrizing the two-body potential and then fitting the additional three-body contribution to the residual forces. The approach is illustrated on liquid water where three-body interactions are essential to reproduce the structural properties, and liquid methanol where two-body interactions are sufficient to reproduce the main structural features of the atomistic system. Furthermore, we demonstrate that the structural and thermodynamic accuracy of the coarse-grained models can be controlled by varying the magnitude of the three-body interactions. Our findings motivate basis set extensions which separate the many-body contributions of different order.

Entities:  

Year:  2018        PMID: 30129962     DOI: 10.1039/c8cp00746b

Source DB:  PubMed          Journal:  Phys Chem Chem Phys        ISSN: 1463-9076            Impact factor:   3.676


  4 in total

Review 1.  Bottom-up Coarse-Graining: Principles and Perspectives.

Authors:  Jaehyeok Jin; Alexander J Pak; Aleksander E P Durumeric; Timothy D Loose; Gregory A Voth
Journal:  J Chem Theory Comput       Date:  2022-09-07       Impact factor: 6.578

2.  A new one-site coarse-grained model for water: Bottom-up many-body projected water (BUMPer). I. General theory and model.

Authors:  Jaehyeok Jin; Yining Han; Alexander J Pak; Gregory A Voth
Journal:  J Chem Phys       Date:  2021-01-28       Impact factor: 3.488

3.  Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting.

Authors:  Stephan Thaler; Julija Zavadlav
Journal:  Nat Commun       Date:  2021-11-25       Impact factor: 14.919

4.  Kernel-Based Machine Learning for Efficient Simulations of Molecular Liquids.

Authors:  Christoph Scherer; René Scheid; Denis Andrienko; Tristan Bereau
Journal:  J Chem Theory Comput       Date:  2020-04-24       Impact factor: 6.006

  4 in total

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