PhyNEO: A Neural-Network-Enhanced Physics-Driven Force Field Development Workflow for Bulk Organic Molecule and Polymer Simulations-Reference-Cited by-同舟云学术

PhyNEO: A Neural-Network-Enhanced Physics-Driven Force Field Development Workflow for Bulk Organic Molecule and Polymer Simulations

Published:2023-12-20 Issue:1 Volume:20 Page:253-265
ISSN:1549-9618
Container-title:Journal of Chemical Theory and Computation
language:en
Short-container-title:J. Chem. Theory Comput.

Author:

Chen Junmin¹^ORCID,Yu Kuang¹²^ORCID

Affiliation:

1. Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, Guangdong 518055, P. R. China

2. Institute of Materials Research (iMR), Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong 518055, P. R. China

Funder

Shenzhen Science and Technology Innovation Commission

National Natural Science Foundation of China

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry,Computer Science Applications

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.3c01045

Reference62 articles.

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

2. Potential energy functions for atomic-level simulations of water and organic and biomolecular systems

3. Development and testing of a general amber force field

4. The determination of an accurate isotope dependent potential energy surface for water from extensive ab initio calculations and experimental data

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