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Copolymers are the products obtained from the polymerization of multiple monomer species. So, in a polymer chain itself, there can be multiple repeating units that come from different monomers. The process of synthesizing a polymer from different monomer species is called copolymerization. When two monomers are involved, the polymer is known as a bipolymer. Polymers with three and four monomers are termed terpolymers and quaterpolymers, respectively. Figure 1 depicts the copolymerization of...

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Polymer Microarrays for High Throughput Discovery of Biomaterials

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Machine Learning-Assisted Identification of Copolymer Microstructures Based on Microscopic Images.

Han Xu¹, Sainan Ma^1,2, Yang Hou¹

¹The State Key Laboratory of Chemical Engineering, College of Chemical and Biological Engineering, Zhejiang University, 38 Zheda Road, Hangzhou310027, China.

ACS Applied Materials & Interfaces

|October 7, 2022

Summary

Machine learning accurately identifies polymer microstructures, even when they are similar. This approach uses transfer learning and feature visualization for explainable results, advancing intelligent polymer research.

Keywords:

glass transition temperature width interpretability machine learning polymer microstructure small data set transfer learning

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Area of Science:

Polymer Science
Materials Science
Machine Learning Applications

Background:

Polymer microstructure is key to material properties.
Distinguishing similar microstructures is challenging.
Effective identification is crucial for material design.

Purpose of the Study:

To apply machine learning for polymer microstructure recognition.
To develop an accurate and interpretable model.
To enable microstructure identification from small experimental datasets.

Main Methods:

Utilized machine learning algorithms.
Employed transfer learning techniques.
Incorporated feature visualization for interpretability.

Main Results:

Achieved high accuracy in microstructure identification.
Developed an interpretable machine learning model.
Model results are explainable through physical chemistry principles.

Conclusions:

Machine learning offers a viable method for polymer microstructure identification.
The developed model is accurate, interpretable, and data-efficient.
This work supports advancements in intelligent polymer research and development.