Deep learning for automated classification and characterization of amorphous materials-Reference-Cited by-同舟云学术

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Deep learning for automated classification and characterization of amorphous materials

Published:2020 Issue:2 Volume:16 Page:435-446
ISSN:1744-683X
Container-title:Soft Matter
language:en
Short-container-title:Soft Matter

Author:

Swanson Kirk¹²³⁴⁵^ORCID,Trivedi Shubhendu⁶⁷⁸⁴⁹,Lequieu Joshua⁵²³⁴¹⁰^ORCID,Swanson Kyle⁶⁷⁸⁴^ORCID,Kondor Risi¹²³⁴¹¹

Affiliation:

1. Department of Computer Science

2. The University of Chicago

3. Chicago

4. USA

5. Pritzker School of Molecular Engineering

6. Computer Science and Artificial Intelligence Laboratory

7. MIT

8. Cambridge

9. Institute for Computational and Experimental Research in Mathematics

10. Materials Research Laboratory

11. Department of Statistics

Abstract

We use deep learning to automatically classify liquid and glass structures and to derive novel metrics that describe glass formation.

Funder

National Science Foundation

Defense Advanced Research Projects Agency

Publisher

Royal Society of Chemistry (RSC)

Subject

Condensed Matter Physics,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2020/SM/C9SM01903K

Reference60 articles.

1. Bond-orientational order in liquids and glasses

2. Applications of local crystal structure measures in experiment and simulation

3. Molecular dynamics study of melting and freezing of small Lennard-Jones clusters

4. Machine learning for autonomous crystal structure identification

5. Machine-learning approach for local classification of crystalline structures in multiphase systems

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