Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials

Xie, Tian; France-Lanord, Arthur; Wang, Yanming; Shao-Horn, Yang; Grossman, Jeffrey C.

doi:10.1038/s41467-019-10663-6

Title: Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials

Abstract

Understanding the dynamical processes that govern the performance of functional materials is essential for the design of next generation materials to tackle global energy and environmental challenges. Many of these processes involve the dynamics of individual atoms or small molecules in condensed phases, e.g. lithium ions in electrolytes, water molecules in membranes, molten atoms at interfaces, etc., which are difficult to understand due to the complexity of local environments. In this work, we develop graph dynamical networks, an unsupervised learning approach for understanding atomic scale dynamics in arbitrary phases and environments from molecular dynamics simulations. We show that important dynamical information, which would be difficult to obtain otherwise, can be learned for various multi-component amorphous material systems. With the large amounts of molecular dynamics data generated every day in nearly every aspect of materials design, this approach provides a broadly applicable, automated tool to understand atomic scale dynamics in material systems.

Authors:

Xie, Tian ^[1]; France-Lanord, Arthur ^[1];

^[1]; Shao-Horn, Yang ^[1];

^[1]

Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)

Publication Date:: Mon Jun 17 00:00:00 EDT 2019

Research Org.:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)

Sponsoring Org.:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities Division

OSTI Identifier:: 1577554

Grant/Contract Number:: AC02-05CH11231; ACI-1053575

Resource Type:: Accepted Manuscript

Journal Name:: Nature Communications

Additional Journal Information:: Journal Volume: 10; Journal Issue: 1; Journal ID: ISSN 2041-1723

Publisher:: Nature Publishing Group

Country of Publication:: United States

Language:: English

Subject:: 36 MATERIALS SCIENCE; Science & Technology - Other Topics

Citation Formats


                    Xie, Tian, France-Lanord, Arthur, Wang, Yanming, Shao-Horn, Yang, and Grossman, Jeffrey C. Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials.  United States: N. p., 2019. 
Web.  doi:10.1038/s41467-019-10663-6.

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                    Xie, Tian, France-Lanord, Arthur, Wang, Yanming, Shao-Horn, Yang, & Grossman, Jeffrey C. Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials.  United States.  https://doi.org/10.1038/s41467-019-10663-6

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                    Xie, Tian, France-Lanord, Arthur, Wang, Yanming, Shao-Horn, Yang, and Grossman, Jeffrey C. Mon .  
"Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials".  United States.  https://doi.org/10.1038/s41467-019-10663-6.  https://www.osti.gov/servlets/purl/1577554.

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@article{osti_1577554,

  title        = {Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials},

  author       = {Xie, Tian and France-Lanord, Arthur and Wang, Yanming and Shao-Horn, Yang and Grossman, Jeffrey C.},

  abstractNote = {Understanding the dynamical processes that govern the performance of functional materials is essential for the design of next generation materials to tackle global energy and environmental challenges. Many of these processes involve the dynamics of individual atoms or small molecules in condensed phases, e.g. lithium ions in electrolytes, water molecules in membranes, molten atoms at interfaces, etc., which are difficult to understand due to the complexity of local environments. In this work, we develop graph dynamical networks, an unsupervised learning approach for understanding atomic scale dynamics in arbitrary phases and environments from molecular dynamics simulations. We show that important dynamical information, which would be difficult to obtain otherwise, can be learned for various multi-component amorphous material systems. With the large amounts of molecular dynamics data generated every day in nearly every aspect of materials design, this approach provides a broadly applicable, automated tool to understand atomic scale dynamics in material systems.},

  doi          = {10.1038/s41467-019-10663-6},

  journal      = {Nature Communications},

  number       = 1,

  volume       = 10,

  place        = {United States},

  year         = {Mon Jun 17 00:00:00 EDT 2019},

  month        = {Mon Jun 17 00:00:00 EDT 2019}

}

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