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

Abstract

Identifying local structure in molecular simulations is of utmost importance. The most common existing approach to identify local structure is to calculate some geometrical quantity referred to as an order parameter. In simple cases order parameters are physically intuitive and trivial to develop (e.g., ion-pair distance), however in most cases, order parameter development becomes a much more difficult endeavor (e.g., crystal structure identification). Using ideas from computer vision, we adapt a specific type of neural network called a PointNet to identify local structural environments in molecular simulations. A primary challenge in applying machine learning techniques to simulation is selecting the appropriate input features. This challenge is system-specific and requires significant human input and intuition. In contrast, our approach is a generic framework that requires no system-specific feature engineering and operates on the raw output of the simulations, i.e., atomic positions. We demonstrate the method on crystal structure identification in Lennard-Jones (four different phases), water (eight different phases), and mesophase (six different phases) systems. The method achieves as high as 99.5% accuracy in crystal structure identification. The method is applicable to heterogeneous nucleation and it can even predict the crystal phases of atoms near external interfaces. We demonstrate the versatility ofmore » our approach by using our method to identify surface hydrophobicity based solely upon positions and orientations of surrounding water molecules. Our results suggest the approach will be broadly applicable to many types of local structure in simulations.« less

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:
Research Org.:
Clemson Univ., SC (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
OSTI Identifier:
1542673
Alternate Identifier(s):
OSTI ID: 1612289
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
Country of Publication:
United Kingdom
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; Chemistry

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. Thu . "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 = {Identifying local structure in molecular simulations is of utmost importance. The most common existing approach to identify local structure is to calculate some geometrical quantity referred to as an order parameter. In simple cases order parameters are physically intuitive and trivial to develop (e.g., ion-pair distance), however in most cases, order parameter development becomes a much more difficult endeavor (e.g., crystal structure identification). Using ideas from computer vision, we adapt a specific type of neural network called a PointNet to identify local structural environments in molecular simulations. A primary challenge in applying machine learning techniques to simulation is selecting the appropriate input features. This challenge is system-specific and requires significant human input and intuition. In contrast, our approach is a generic framework that requires no system-specific feature engineering and operates on the raw output of the simulations, i.e., atomic positions. We demonstrate the method on crystal structure identification in Lennard-Jones (four different phases), water (eight different phases), and mesophase (six different phases) systems. The method achieves as high as 99.5% accuracy in crystal structure identification. The method is applicable to heterogeneous nucleation and it can even predict the crystal phases of atoms near external interfaces. We demonstrate the versatility of our approach by using our method to identify surface hydrophobicity based solely upon positions and orientations of surrounding water molecules. Our results suggest the approach will be broadly applicable to many types of local structure in simulations.},
doi = {10.1039/C9SC02097G},
journal = {Chemical Science},
number = 32,
volume = 10,
place = {United Kingdom},
year = {2019},
month = {7}
}

Journal Article:
Free Publicly Available Full Text
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DOI: 10.1039/C9SC02097G

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Cited by: 5 works
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