Many-Body Neural Network-Based Force Field for Structure-Based Coarse-Graining of Water
Journal Article
·
· Journal of Physical Chemistry. A, Molecules, Spectroscopy, Kinetics, Environment, and General Theory
- Department of Mechanical Science and Engineering, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
- Oden Institute for Computational Engineering and Sciences, Walker Department of Mechanical Engineering, The University of Texas at Austin, Austin, Texas 78712, United States
Not provided.
- Research Organization:
- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC)
- DOE Contract Number:
- SC0019112
- OSTI ID:
- 1977952
- Journal Information:
- Journal of Physical Chemistry. A, Molecules, Spectroscopy, Kinetics, Environment, and General Theory, Vol. 126, Issue 12; ISSN 1089-5639
- Publisher:
- American Chemical Society
- Country of Publication:
- United States
- Language:
- English
Similar Records
A coarse-grain force field based on quantum mechanics (CGq FF) for molecular dynamics simulation of poly(ethylene glycol)-block-poly(ε-caprolactone) (PEG-b-PCL) micelles
A Reactive Force Field with Coarse-Grained Electrons for Liquid Water
Coarse-grained molecular dynamics integrated with convolutional neural network for comparing shapes of temperature sensitive bottlebrushes
Journal Article
·
2020
· Physical Chemistry Chemical Physics. PCCP
·
OSTI ID:1852404
A Reactive Force Field with Coarse-Grained Electrons for Liquid Water
Journal Article
·
2020
· Journal of Physical Chemistry Letters
·
OSTI ID:1838625
+1 more
Coarse-grained molecular dynamics integrated with convolutional neural network for comparing shapes of temperature sensitive bottlebrushes
Journal Article
·
2022
· npj Computational Materials
·
OSTI ID:1982114