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This content will become publicly available on July 16, 2019

Title: DeePCG: Constructing coarse-grained models via deep neural networks

Authors:
ORCiD logo [1] ; ORCiD logo [1] ; ORCiD logo [2] ;  [3] ;  [4]
  1. Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544, USA
  2. Institute of Applied Physics and Computational Mathematics, Fenghao East Road 2, Beijing 100094, People’s Republic of China and CAEP Software Center for High Performance Numerical Simulation, Huayuan Road 6, Beijing 100088, People’s Republic of China
  3. Department of Chemistry, Department of Physics, Program in Applied and Computational Mathematics, Princeton Institute for the Science and Technology of Materials, Princeton University, Princeton, New Jersey 08544, USA
  4. Department of Mathematics and Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544, USA and Beijing Institute of Big Data Research, Beijing 100871, People’s Republic of China
Publication Date:
Grant/Contract Number:
SC0008626; SC0009248
Type:
Publisher's Accepted Manuscript
Journal Name:
Journal of Chemical Physics
Additional Journal Information:
Journal Volume: 149; Journal Issue: 3; Related Information: CHORUS Timestamp: 2018-07-16 13:40:35; Journal ID: ISSN 0021-9606
Publisher:
American Institute of Physics
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
OSTI Identifier:
1460500