Exploring order parameters and dynamic processes in disordered systems via variational autoencoders
Abstract
We suggest and implement an approach for the bottom-up description of systems undergoing large-scale structural changes and chemical transformations from dynamic atomically resolved imaging data, where only partial or uncertain data on atomic positions are available. This approach is predicated on the synergy of two concepts, the parsimony of physical descriptors and general rotational invariance of noncrystalline solids, and is implemented using a rotationally invariant extension of the variational autoencoder applied to semantically segmented atom-resolved data seeking the most effective reduced representation for the system that still contains the maximum amount of original information. This approach allowed us to explore the dynamic evolution of electron beam–induced processes in a silicon-doped graphene system, but it can be also applied for a much broader range of atomic scale and mesoscopic phenomena to introduce the bottom-up order parameters and explore their dynamics with time and in response to external stimuli.
- Authors:
-
[1];
[1];
[1];
[2]
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.
- Publication Date:
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- OSTI Identifier:
- 1824874
- Alternate Identifier(s):
- OSTI ID: 1783050
- Grant/Contract Number:
- AC05-00OR22725
- Resource Type:
- Published Article
- Journal Name:
- Science Advances
- Additional Journal Information:
- Journal Name: Science Advances Journal Volume: 7 Journal Issue: 17; Journal ID: ISSN 2375-2548
- Publisher:
- American Association for the Advancement of Science (AAAS)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE; 46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY
Citation Formats
Kalinin, Sergei V., Dyck, Ondrej, Jesse, Stephen, and Ziatdinov, Maxim. Exploring order parameters and dynamic processes in disordered systems via variational autoencoders. United States: N. p., 2021.
Web. doi:10.1126/sciadv.abd5084.
Kalinin, Sergei V., Dyck, Ondrej, Jesse, Stephen, & Ziatdinov, Maxim. Exploring order parameters and dynamic processes in disordered systems via variational autoencoders. United States. https://doi.org/10.1126/sciadv.abd5084
Kalinin, Sergei V., Dyck, Ondrej, Jesse, Stephen, and Ziatdinov, Maxim. Fri .
"Exploring order parameters and dynamic processes in disordered systems via variational autoencoders". United States. https://doi.org/10.1126/sciadv.abd5084.
@article{osti_1824874,
title = {Exploring order parameters and dynamic processes in disordered systems via variational autoencoders},
author = {Kalinin, Sergei V. and Dyck, Ondrej and Jesse, Stephen and Ziatdinov, Maxim},
abstractNote = {We suggest and implement an approach for the bottom-up description of systems undergoing large-scale structural changes and chemical transformations from dynamic atomically resolved imaging data, where only partial or uncertain data on atomic positions are available. This approach is predicated on the synergy of two concepts, the parsimony of physical descriptors and general rotational invariance of noncrystalline solids, and is implemented using a rotationally invariant extension of the variational autoencoder applied to semantically segmented atom-resolved data seeking the most effective reduced representation for the system that still contains the maximum amount of original information. This approach allowed us to explore the dynamic evolution of electron beam–induced processes in a silicon-doped graphene system, but it can be also applied for a much broader range of atomic scale and mesoscopic phenomena to introduce the bottom-up order parameters and explore their dynamics with time and in response to external stimuli.},
doi = {10.1126/sciadv.abd5084},
journal = {Science Advances},
number = 17,
volume = 7,
place = {United States},
year = {Fri Apr 23 00:00:00 EDT 2021},
month = {Fri Apr 23 00:00:00 EDT 2021}
}
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