Machine Learning Models Predicting Solubility and Polymerizability of Polyimides Considering Multiple Monomers for CO₂ Separation Membranes.

Authors

Yuto Shino, Motosuke Katayama, Yuri Ito, Hiromasa Kaneko

Published in

Molecular informatics. Volume 45. Issue 4. Pages e70032.

Abstract

Membrane technologies for the separation of gases, such as CO₂/CH₄ mixtures, have attracted attention because of their high energy efficiency. Polyimides are considered promising membrane materials for CO₂ separation, and there is a growing demand for materials with even higher performance. In the screening of candidate materials, it is essential to consider not only separation performance but also solubility and polymerizability during the synthesis process. Low solubility or polymerizability can inhibit membrane fabrication and the evaluation of separation performance, potentially leading to wasted resources and effort. In this study, we developed machine learning models to predict the solubility and polymerizability of polyimides. Mixture features derived from molecular descriptors of multiple monomers and mixing ratios were used as inputs for the classification models. The models were then applied to novel candidates, and their effectiveness was validated experimentally.

PMID:
42017814
Bibliographic data and abstract were imported from PubMed on 12 Sep 2026.

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