Neural-network-based order parameters for classification of binary hard-sphere crystal structures. (17th November 2018)

• Record Type: Journal Article
• Authors: Boattini, Emanuele; Ram, Michel; Smallenburg, Frank; Filion, Laura
• Is Part Of: Molecular physics. Volume 116:Number 21/22(2018)
• Page Start: 3066
• Page End: 3075
• Publication Date: 2018-11-17
• Subjects: Binary hard spheres -- neural networks -- order parameters -- colloids; Molecules -- Periodicals; Chemistry, Physical and theoretical -- Periodicals; Molécules -- Périodiques; Chimie physique et théorique -- Périodiques; 539.6.05
• DOI: 10.1080/00268976.2018.1483537
• Languages: English
• ISSNs: 0026-8976

Abstract

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 for all phases, meaning that our method is completely general and able to capture structural differences of a wide range of binary crystals. GRAPHICAL ABSTRACT: