Machine learning in constructing structure–property relationships of polymers

Chemical Physics Reviews, Journal Year: 2025, Volume and Issue: 6(2)

Published: May 1, 2025

The properties of polymer materials are closely related to their structures. A deep understanding quantitative relationships between the structures and polymers is crucial for design preparation high-performance materials. However, these inherently complex difficult model with limited trial error experimental data. In recent years, machine learning (ML) has become an effective multidimensional relationship modeling method, playing important role in construction This review first provides overview ML workflow, a focus on feature engineering commonly used algorithms application processes. Afterward, progress was summarized evaluated from aspects mechanical properties, thermal conductivity, glass transition temperature (Tg), compatibility, dielectric refractive index polymers. Finally, prospects material research were proposed.

Language: Английский

Machine Learning in Polymeric Technical Textiles: A Review

Polymers, Journal Year: 2025, Volume and Issue: 17(9), P. 1172 - 1172

Published: April 25, 2025

The integration of machine learning (ML) has begun to reshape the development advanced polymeric materials used in technical textiles. Polymeric materials, with their versatile properties, are central performance textiles across industries such as healthcare, aerospace, automotive, and construction. By utilizing ML AI, researchers now able design optimize polymers for specific applications more efficiently, predict behavior under extreme conditions, develop smart, responsive that enhance functionality. This review highlights transformative potential polymer-based textiles, enabling advancements waste sorting (with classification accuracy up 100% pure fibers), material (predicting stiffness properties within 10% error), defect prediction (enabling proactive interventions fabric production), smart wearable systems (achieving response times low 192 ms physiological monitoring). AI technologies drives sustainable innovation enhances functionality textile products. Through case studies examples, this provides guidance future research using technologies.

Language: Английский