A machine learning approach to analyze the structural formation of soft matter via image recognition. Issue 2 (2nd July 2020)

• Record Type: Journal Article
• Authors: Terao, Takamichi
• Is Part Of: Soft materials. Volume 18:Issue 2-3(2020)
• Page Start: 215
• Page End: 227
• Publication Date: 2020-07-02
• Subjects: Convolutional neural networks -- colloidal crystals -- structural formation; Materials science -- Periodicals; 620.11
• DOI: 10.1080/1539445X.2020.1715433
• Languages: English
• ISSNs: 1539-445X

Abstract

ABSTRACT: A novel method is developed to analyze the structural formation of colloidal particles based on image recognition via a convolutional neural network (CNN). This makes it possible to analyze various complex structures that are difficult to study using a traditional bond-order parameter analysis. Molecular dynamics simulations on soft colloidal systems are performed in quasi two-dimensional and three-dimensional systems, and the efficiency of the proposed method is demonstrated.