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Title: A generalized deep learning approach for local structure identification in molecular simulations

Abstract

We demonstrate a PointNet-based deep learning approach to classify local structure in molecular simulations, learning features directly from atomic coordinates.

Authors:
ORCiD logo [1];  [2];  [1]; ORCiD logo [2]; ORCiD logo [1]
  1. Department of Chemical & Biomolecular Engineering, Clemson University, Clemson, USA
  2. Department of Electrical & Computer Engineering, Clemson University, Clemson, USA
Publication Date:
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1542673
Grant/Contract Number:  
SC0015448
Resource Type:
Published Article
Journal Name:
Chemical Science
Additional Journal Information:
Journal Name: Chemical Science Journal Volume: 10 Journal Issue: 32; Journal ID: ISSN 2041-6520
Publisher:
Royal Society of Chemistry (RSC)
Country of Publication:
United Kingdom
Language:
English

Citation Formats

DeFever, Ryan S., Targonski, Colin, Hall, Steven W., Smith, Melissa C., and Sarupria, Sapna. A generalized deep learning approach for local structure identification in molecular simulations. United Kingdom: N. p., 2019. Web. doi:10.1039/C9SC02097G.
DeFever, Ryan S., Targonski, Colin, Hall, Steven W., Smith, Melissa C., & Sarupria, Sapna. A generalized deep learning approach for local structure identification in molecular simulations. United Kingdom. doi:10.1039/C9SC02097G.
DeFever, Ryan S., Targonski, Colin, Hall, Steven W., Smith, Melissa C., and Sarupria, Sapna. Wed . "A generalized deep learning approach for local structure identification in molecular simulations". United Kingdom. doi:10.1039/C9SC02097G.
@article{osti_1542673,
title = {A generalized deep learning approach for local structure identification in molecular simulations},
author = {DeFever, Ryan S. and Targonski, Colin and Hall, Steven W. and Smith, Melissa C. and Sarupria, Sapna},
abstractNote = {We demonstrate a PointNet-based deep learning approach to classify local structure in molecular simulations, learning features directly from atomic coordinates.},
doi = {10.1039/C9SC02097G},
journal = {Chemical Science},
number = 32,
volume = 10,
place = {United Kingdom},
year = {2019},
month = {8}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1039/C9SC02097G

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Works referenced in this record:

Molecular dynamics study of melting and freezing of small Lennard-Jones clusters
journal, September 1987

  • Honeycutt, J. Dana.; Andersen, Hans C.
  • The Journal of Physical Chemistry, Vol. 91, Issue 19, p. 4950-4963
  • DOI: 10.1021/j100303a014

A simple method for displaying the hydropathic character of a protein
journal, May 1982