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Title: A Framework to Learn Physics from Atomically Resolved Images

Abstract

Here, we present a generalized framework for physics extraction, i.e., knowledge, from atomically resolved images, and show its utility by applying it to a model system of segregation of chalcogen atoms in an FeSe 0.45Te 0.55 superconductor system. We emphasize that the framework can be used for any imaging data for which a generative physical model exists. Consider that a generative physical model can produce a very large number of configurations, not all of which are observable. By applying a microscope function to a sub-set of this generated data, we form a simulated dataset on which statistics can be computed.

Authors:
 [1];  [2];  [3];  [1];  [1];  [1]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States)
  3. Huazhong Univ. of Science and Technology, Wuhan (China)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1399519
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Journal Article
Journal Name:
Microscopy and Microanalysis
Additional Journal Information:
Journal Volume: 23; Journal Issue: S1; Journal ID: ISSN 1431-9276
Publisher:
Microscopy Society of America (MSA)
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS

Citation Formats

Vlcek, L., Maksov, A., Pan, M., Jesse, S., Kalinin, Sergei V., and Vasudevan, R. K. A Framework to Learn Physics from Atomically Resolved Images. United States: N. p., 2017. Web. doi:10.1017/S1431927617001209.
Vlcek, L., Maksov, A., Pan, M., Jesse, S., Kalinin, Sergei V., & Vasudevan, R. K. A Framework to Learn Physics from Atomically Resolved Images. United States. doi:10.1017/S1431927617001209.
Vlcek, L., Maksov, A., Pan, M., Jesse, S., Kalinin, Sergei V., and Vasudevan, R. K. Fri . "A Framework to Learn Physics from Atomically Resolved Images". United States. doi:10.1017/S1431927617001209.
@article{osti_1399519,
title = {A Framework to Learn Physics from Atomically Resolved Images},
author = {Vlcek, L. and Maksov, A. and Pan, M. and Jesse, S. and Kalinin, Sergei V. and Vasudevan, R. K.},
abstractNote = {Here, we present a generalized framework for physics extraction, i.e., knowledge, from atomically resolved images, and show its utility by applying it to a model system of segregation of chalcogen atoms in an FeSe0.45Te0.55 superconductor system. We emphasize that the framework can be used for any imaging data for which a generative physical model exists. Consider that a generative physical model can produce a very large number of configurations, not all of which are observable. By applying a microscope function to a sub-set of this generated data, we form a simulated dataset on which statistics can be computed.},
doi = {10.1017/S1431927617001209},
journal = {Microscopy and Microanalysis},
issn = {1431-9276},
number = S1,
volume = 23,
place = {United States},
year = {2017},
month = {8}
}

Works referenced in this record:

Direct Probe of Interplay between Local Structure and Superconductivity in FeTe 0.55 Se 0.45
journal, March 2013

  • Lin, Wenzhi; Li, Qing; Sales, Brian C.
  • ACS Nano, Vol. 7, Issue 3
  • DOI: 10.1021/nn400012q