Machine Learning of First-Principles Force-Fields for Alkane and Polyene Hydrocarbons-Reference-Cited by-同舟云学术

Machine Learning of First-Principles Force-Fields for Alkane and Polyene Hydrocarbons

Published:2021-10-18 Issue:42 Volume:125 Page:9414-9420
ISSN:1089-5639
Container-title:The Journal of Physical Chemistry A
language:en
Short-container-title:J. Phys. Chem. A

Author:

Hajibabaei Amir¹^ORCID,Ha Miran¹^ORCID,Pourasad Saeed¹,Kim Junu¹^ORCID,Kim Kwang S.¹^ORCID

Affiliation:

1. Center for Superfunctional Materials, Department of Chemistry, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Ulsan 44919, Korea

Funder

National Research Foundation of Korea

Korea Institute of Science and Technology Information

UNIST A.I. Incubation Project Fund

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpca.1c05819

Reference45 articles.

1. Optimized intermolecular potential functions for liquid hydrocarbons

2. Molecular dynamics simulations of a fluid bilayer of dipalmitoylphosphatidylcholine at full hydration, constant pressure, and constant temperature

3. Gas-phase and liquid-state properties of esters, nitriles, and nitro compounds with the OPLS-AA force field

4. Development and Testing of the OPLS All-Atom Force Field on Conformational Energetics and Properties of Organic Liquids

5. Optimization of the OPLS-AA Force Field for Long Hydrocarbons

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