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

Machine-learning approach for local classification of crystalline structures in multiphase systems Machine learning is one of the most popular fields in computer science and has a vast number of applications. In this work we will propose a method that will use a neural network to locally identify crystal structures in a mixed phase Yukawa system consisting of fcc, hcp, and bcc clusters and disordered particles similar to plasma crystals. We compare our approach to already used methods and show that the quality of identification increases significantly. The technique works very well for highly disturbed lattices and shows a flexible and robust way to classify crystalline structures that can be used by only providing particle positions. This leads to insights into highly disturbed crystalline structures. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Physical Review E American Physical Society (APS)

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

Physical Review E , Volume 96 (1) – Jul 19, 2017

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

Physical Review E , Volume 96 (1) – Jul 19, 2017

Abstract

Machine learning is one of the most popular fields in computer science and has a vast number of applications. In this work we will propose a method that will use a neural network to locally identify crystal structures in a mixed phase Yukawa system consisting of fcc, hcp, and bcc clusters and disordered particles similar to plasma crystals. We compare our approach to already used methods and show that the quality of identification increases significantly. The technique works very well for highly disturbed lattices and shows a flexible and robust way to classify crystalline structures that can be used by only providing particle positions. This leads to insights into highly disturbed crystalline structures.

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Publisher
American Physical Society (APS)
Copyright
Copyright © ©2017 American Physical Society
ISSN
1539-3755
eISSN
550-2376
DOI
10.1103/PhysRevE.96.011301
pmid
29347271
Publisher site
See Article on Publisher Site

Abstract

Machine learning is one of the most popular fields in computer science and has a vast number of applications. In this work we will propose a method that will use a neural network to locally identify crystal structures in a mixed phase Yukawa system consisting of fcc, hcp, and bcc clusters and disordered particles similar to plasma crystals. We compare our approach to already used methods and show that the quality of identification increases significantly. The technique works very well for highly disturbed lattices and shows a flexible and robust way to classify crystalline structures that can be used by only providing particle positions. This leads to insights into highly disturbed crystalline structures.

Journal

Physical Review EAmerican Physical Society (APS)

Published: Jul 19, 2017

There are no references for this article.