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Machine learning for autonomous crystal structure identification

Published:2017 Issue:27 Volume:13 Page:4733-4745
ISSN:1744-683X
Container-title:Soft Matter
language:en
Short-container-title:Soft Matter

Author:

Reinhart Wesley F.¹²³⁴^ORCID,Long Andrew W.⁵⁶⁷⁴,Howard Michael P.¹²³⁴^ORCID,Ferguson Andrew L.⁵⁶⁷⁴⁸^ORCID,Panagiotopoulos Athanassios Z.¹²³⁴^ORCID

Affiliation:

1. Department of Chemical and Biological Engineering

2. Princeton University

3. Princeton

4. USA

5. Department of Materials Science and Engineering

6. University of Illinois at Urbana-Champaign

7. Urbana

8. Department of Chemical and Biomolecular Engineering

Abstract

Machine learning enables the identification of crystalline domains without the need to specify target structures ahead of time.

Funder

National Science Foundation

Publisher

Royal Society of Chemistry (RSC)

Subject

Condensed Matter Physics,General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2017/SM/C7SM00957G

Reference60 articles.

1. Self-assembly and applications of anisotropic nanomaterials: A review

2. Metal–Organic Frameworks as Platforms for Functional Materials

3. Self-Assembly of Colloidal Nanocrystals: From Intricate Structures to Functional Materials

4. Design and functionality of colloidal-crystal-templated materials—chemical applications of inverse opals

5. Phase behaviour of concentrated suspensions of nearly hard colloidal spheres

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