Characterising soft matter using machine learning.

Area of Science:

• Soft Matter Physics
• Materials Science
• Computational Materials Science

Background:

• Machine learning (ML) is increasingly influential in materials research.
• Soft matter systems present unique challenges for analysis and design.

Purpose of the Study:

• To review the current applications and progress of machine learning in soft matter research.
• To highlight successes and remaining challenges in ML-driven materials discovery.

Main Methods:

• Application of ML, particularly convolutional neural networks, for particle tracking.
• Utilizing ML for describing, classifying, and interpreting ordered particle arrangements.
• Employing ML to quantify structure-property relationships in glass research.
• Exploring ML for the design of composite soft materials.

Main Results:

• ML demonstrates impressive performance in particle tracking, though challenges persist.
• ML techniques successfully characterize ordered particle arrangements and provide interpretations.
• ML has been decisive in identifying subtle correlations in glass structures related to rearrangements.
• Emerging ML applications show promise in designing novel composite soft materials.

Conclusions:

• Machine learning is a transformative tool in soft matter research, enhancing analysis and discovery.
• ML facilitates deeper understanding of material properties and accelerates the design of new materials.
• Future work should focus on addressing ML limitations and expanding its role in materials design and extrapolation.