麻豆淫院

A study in npj Computational Materials presents a new AI system that uses computer vision and language processing to interpret complex polymer鈥搒olvent interactions such as swelling, gelation and dispersion from images and videos.

The paper is titled "A multi-model vision assistant for autonomous interpretation of polymer鈥搒olvent solvation behaviors."

Polymer鈥搒olvent systems are notoriously tricky to analyze due to the variety of behaviors involved and the subjective nature of manual assessments. This new approach integrates multiple AI models鈥攊ncluding convolutional neural networks to understand static and dynamic visual data and a vision鈥搇anguage module that generates descriptive captions鈥攑roviding an objective, scalable way to track and describe solvation phenomena.

"Polymers and solvents don't always behave predictably and human evaluations can vary," said Liew. "Our AI assistant can see what's happening in detail and put it into words, making it easier to analyze data quickly and reliably鈥攅specially for high-throughput experiments."

This system promises to accelerate materials discovery by enabling automated, repeatable interpretation of experimental results, removing bottlenecks caused by manual screening.

The work is part of Ph.D. research by Zheng Jie Liew, supported by Ziad Elkhaiary, who contributed to the project while completing the department's Advanced Chemical Engineering (ACE) Master's, under the supervision of Professor Alexei A. Lapkin in the Sustainable Reaction Engineering research group at the University of Cambridge's Department of Chemical Engineering and Biotechnology.

Elkhaiary's contribution during his ACE Master's highlights the research-led teaching ethos of the department.

"It's rewarding to see our students actively shaping cutting-edge science," said Lapkin. "Contributing to real projects prepares them for the challenges of sustainable chemical engineering."