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Self-assembling Morphologies Obtained from Helical Polycarbodiimide Copolymers and Their Triazole Derivatives

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Machine Learning Models Predicting Solubility and Polymerizability of Polyimides Considering Multiple Monomers for

Yuto Shino¹, Motosuke Katayama², Yuri Ito²

¹Department of Applied Chemistry, School of Science and Technology, Meiji University, Kawasaki, Kanagawa, Japan.

Molecular Informatics

|April 22, 2026

Summary

Machine learning models predict polyimide solubility and polymerizability for efficient CO2/CH4 separation membranes. This accelerates the discovery of high-performance materials, reducing wasted resources in membrane development.

Keywords:

cheminformatics copolymerization machine learning membranes polymers

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Area of Science:

Materials Science
Chemical Engineering
Computational Chemistry

Background:

Membrane technology offers energy-efficient gas separation, particularly for CO2/CH4 mixtures.
Polyimides are promising for CO2 separation, but higher performance materials are needed.
Material solubility and polymerizability are critical for membrane fabrication and performance evaluation.

Purpose of the Study:

To develop machine learning models for predicting polyimide solubility and polymerizability.
To facilitate the screening of novel polyimide candidates for CO2 separation membranes.
To reduce resource expenditure in the development of advanced membrane materials.

Main Methods:

Utilized mixture features derived from molecular descriptors of monomers and their mixing ratios.
Developed classification models to predict solubility and polymerizability.
Applied predictive models to novel polyimide candidates and validated findings experimentally.

Main Results:

Successfully developed machine learning models capable of predicting polyimide solubility and polymerizability.
Demonstrated the effectiveness of the models in identifying promising novel polyimide candidates.
Experimental validation confirmed the predictive accuracy of the developed models.

Conclusions:

Machine learning accelerates the identification of suitable polyimide materials for CO2 separation membranes.
Predictive modeling of solubility and polymerizability is crucial for efficient membrane material development.
This approach streamlines the discovery process, saving time and resources.