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Title: Hierarchical modeling of molecular energies using a deep neural network

Authors:
ORCiD logo [1] ; ORCiD logo [2] ; ORCiD logo [1]
  1. Theoretical Division and CNLS, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA
  2. Theoretical Division and CNLS, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA, Department of Chemistry, University of Florida, Gainesville, Florida 32611, USA
Publication Date:
Type:
Publisher's Accepted Manuscript
Journal Name:
Journal of Chemical Physics
Additional Journal Information:
Journal Name: Journal of Chemical Physics Journal Volume: 148 Journal Issue: 24; Journal ID: ISSN 0021-9606
Publisher:
American Institute of Physics
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
OSTI Identifier:
1426848

Lubbers, Nicholas, Smith, Justin S., and Barros, Kipton. Hierarchical modeling of molecular energies using a deep neural network. United States: N. p., Web. doi:10.1063/1.5011181.
Lubbers, Nicholas, Smith, Justin S., & Barros, Kipton. Hierarchical modeling of molecular energies using a deep neural network. United States. doi:10.1063/1.5011181.
Lubbers, Nicholas, Smith, Justin S., and Barros, Kipton. 2018. "Hierarchical modeling of molecular energies using a deep neural network". United States. doi:10.1063/1.5011181.
@article{osti_1426848,
title = {Hierarchical modeling of molecular energies using a deep neural network},
author = {Lubbers, Nicholas and Smith, Justin S. and Barros, Kipton},
abstractNote = {},
doi = {10.1063/1.5011181},
journal = {Journal of Chemical Physics},
number = 24,
volume = 148,
place = {United States},
year = {2018},
month = {6}
}