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Title: Learning free energy landscapes using artificial neural networks

Authors:
 [1]; ORCiD logo [1]
  1. Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, Indiana 46556, USA
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1425538
Resource Type:
Journal Article: Publisher's Accepted Manuscript
Journal Name:
Journal of Chemical Physics
Additional Journal Information:
Journal Name: Journal of Chemical Physics Journal Volume: 148 Journal Issue: 10; Journal ID: ISSN 0021-9606
Publisher:
American Institute of Physics
Country of Publication:
United States
Language:
English

Citation Formats

Sidky, Hythem, and Whitmer, Jonathan K. Learning free energy landscapes using artificial neural networks. United States: N. p., 2018. Web. doi:10.1063/1.5018708.
Sidky, Hythem, & Whitmer, Jonathan K. Learning free energy landscapes using artificial neural networks. United States. doi:10.1063/1.5018708.
Sidky, Hythem, and Whitmer, Jonathan K. Wed . "Learning free energy landscapes using artificial neural networks". United States. doi:10.1063/1.5018708.
@article{osti_1425538,
title = {Learning free energy landscapes using artificial neural networks},
author = {Sidky, Hythem and Whitmer, Jonathan K.},
abstractNote = {},
doi = {10.1063/1.5018708},
journal = {Journal of Chemical Physics},
number = 10,
volume = 148,
place = {United States},
year = {Wed Mar 14 00:00:00 EDT 2018},
month = {Wed Mar 14 00:00:00 EDT 2018}
}

Journal Article:
Free Publicly Available Full Text
This content will become publicly available on March 12, 2019
Publisher's Accepted Manuscript

Citation Metrics:
Cited by: 2 works
Citation information provided by
Web of Science

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Works referenced in this record:

Large-scale screening of hypothetical metal–organic frameworks
journal, November 2011

  • Wilmer, Christopher E.; Leaf, Michael; Lee, Chang Yeon
  • Nature Chemistry, Vol. 4, Issue 2, p. 83-89
  • DOI: 10.1038/nchem.1192