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

被引:48
作者
Dietz, C. [1 ]
Kretz, T. [1 ]
Thoma, M. H. [1 ]
机构
[1] Justus Liebig Univ Giessen, Phys Inst 1, Heinrich Buff Ring 16, D-35392 Giessen, Germany
关键词
BOND-ORIENTATIONAL ORDER; NUCLEATION;
D O I
10.1103/PhysRevE.96.011301
中图分类号
O35 [流体力学]; O53 [等离子体物理学];
学科分类号
070204 ; 080103 ; 080704 ;
摘要
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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页数:5
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