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Synthesis and Characterization of Supramolecular Colloids

Synthesis and Characterization of Supramolecular Colloids

Published on: April 22, 2016

Semi-supervised learning for the study of structural formation in colloidal systems via image recognition.

Takamichi Terao¹

¹Department of Electrical, Electronic and Computer Engineering, Gifu University, Gifu, Japan.

Journal of Physics. Condensed Matter : an Institute of Physics Journal

|May 7, 2021

Summary

This study introduces a hybrid machine learning approach combining supervised and unsupervised learning for analyzing colloidal systems. The novel method effectively identifies both known and unknown structural phases in colloidal systems.

Keywords:

colloidal systems convolutional neural networks local bond-order parameters machine learning semi-supervised learning soft matter

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Area of Science:

Colloidal science
Materials science
Computational physics

Background:

Machine learning is increasingly used for analyzing colloidal system structures.
Local bond-order parameters (LBOPs) are effective for crystal structures but limited for random or unknown phases.
Image-based convolutional neural networks (CNNs) can identify various structures but struggle with novel phases in supervised learning.

Purpose of the Study:

To develop a robust machine learning framework for analyzing colloidal system structures, capable of identifying both known and unknown phases.
To overcome the limitations of purely supervised learning methods when encountering novel structural configurations.

Main Methods:

A hybrid machine learning scheme integrating supervised and unsupervised learning techniques was proposed.
The approach utilizes both image-based convolutional neural networks (CNNs) and generalized local bond-order parameters (LBOPs).
The method was applied to analyze two-dimensional colloidal systems.

Main Results:

The proposed hybrid scheme demonstrated significant efficiency in analyzing colloidal system structures.
The combined approach successfully identified various structural phases, including potentially unknown ones.
The method offers a more comprehensive analysis compared to traditional techniques.

Conclusions:

The hybrid supervised and unsupervised learning approach provides an effective solution for analyzing colloidal system structures.
This method enhances the ability to detect and classify diverse structural phases, advancing the field of machine learning in materials science.