Utilizing Machine Learning for Efficient Parameterization of Coarse Grained Molecular Force Fields
Author:
Affiliation:
1. IBM Research U.K., Hartree Centre, Daresbury WA4 4AD, United Kingdom
2. STFC Daresbury Laboratories, Daresbury WA4 4AD, United Kingdom
Funder
Department for Business, Energy and Industrial Strategy
Publisher
American Chemical Society (ACS)
Subject
Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry
Link
https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.9b00646
Reference69 articles.
1. Unified Approach for Molecular Dynamics and Density-Functional Theory
2. Ab initiomolecular dynamics: basic concepts, current trends and novel applications
3. CHARMM: A program for macromolecular energy, minimization, and dynamics calculations
4. AMBER, a package of computer programs for applying molecular mechanics, normal mode analysis, molecular dynamics and free energy calculations to simulate the structural and energetic properties of molecules
5. The MARTINI Force Field: Coarse Grained Model for Biomolecular Simulations
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