Modeling of Peptides with Classical and Novel Machine Learning Force Fields: A Comparison-Reference-Cited by-同舟云学术

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Modeling of Peptides with Classical and Novel Machine Learning Force Fields: A Comparison

Published:2021-04-02 Issue:14 Volume:125 Page:3598-3612
ISSN:1520-6106
Container-title:The Journal of Physical Chemistry B
language:en
Short-container-title:J. Phys. Chem. B

Author:

Rosenberger David¹²^ORCID,Smith Justin S.¹,Garcia Angel E.²^ORCID

Affiliation:

1. Los Alamos National Laboratory, Theoretical Division, Chemistry and Physics of Materials Group, Los Alamos, 87545 New Mexico, United States

2. Los Alamos National Laboratory, Theoretical Division, Center for Nonlinear Studies, Los Alamos, 87545 New Mexico, United States

Funder

Laboratory Directed Research and Development

Publisher

American Chemical Society (ACS)

Subject

Materials Chemistry,Surfaces, Coatings and Films,Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpcb.0c10401

Reference98 articles.

1. Studies in Molecular Dynamics. I. General Method

2. Coupled-cluster theory in quantum chemistry

3. Self-Consistent Equations Including Exchange and Correlation Effects

4. Unified Approach for Molecular Dynamics and Density-Functional Theory

5. Ab initio theory and modeling of water

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