Back to Search
Start Over
Neural-network-based order parameters for classification of binary hard-sphere crystal structures.
- Source :
-
Molecular Physics . Nov2018, Vol. 116 Issue 21/22, p3066-3075. 10p. - Publication Year :
- 2018
-
Abstract
- Identifying crystalline structures is a common challenge in many types of research. Here, we focus on binary mixtures of hard spheres of various size ratios, which stabilise a range of crystal structures with varying complexity. We train feed-forward neural networks to distinguish different crystalline and fluid environments on a single-particle basis, by analysing vectors composed of several averaged local bond order parameters. For all size ratios considered, we achieve a classification accuracy above <inline-graphic></inline-graphic> for all phases, meaning that our method is completely general and able to capture structural differences of a wide range of binary crystals. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00268976
- Volume :
- 116
- Issue :
- 21/22
- Database :
- Academic Search Index
- Journal :
- Molecular Physics
- Publication Type :
- Academic Journal
- Accession number :
- 131778737
- Full Text :
- https://doi.org/10.1080/00268976.2018.1483537