Machine Learning for Polymer Chemical Resistance to Organic Solvents

Shogo Kunieda; Mitsuru Yambe; Hiromori Murashima; Takeru Nakamura; Toshiaki Shintani; Hitoshi Kamijima; Yoshihiro Hayashi; Yosuke Hanawa; Ryo Yoshida

arXiv · 2509.05344

Machine Learning for Polymer Chemical Resistance to Organic Solvents

Shogo Kunieda·Mitsuru Yambe·Hiromori Murashima·Takeru Nakamura·Toshiaki Shintani·Hitoshi Kamijima·Yoshihiro Hayashi·Yosuke Hanawa·Ryo Yoshida

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Abstract

Predicting the chemical resistance of polymers to organic solvents is a longstanding challenge in materials science, with significant implications for sustainable materials design and industrial applications. Here, we address the need for interpretable and generalizable frameworks to understand and predict polymer chemical resistance beyond conventional solubility models. We systematically analyze a large dataset of polymer solvent combinations using a data-driven approach. Our study reveals that polymer crystallinity and density, as well as solvent polarity, are key factors governing chemical resistance, and that these trends are consistent with established theoretical models. These findings provide a foundation for rational screening and design of polymer materials with tailored chemical resistance, advancing both fundamental understanding and practical applications.

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Shogo Kunieda, Mitsuru Yambe, Hiromori Murashima, Takeru Nakamura, Toshiaki Shintani, Hitoshi Kamijima, Yoshihiro Hayashi, Yosuke Hanawa, Ryo Yoshida. 2025-09-02. Machine Learning for Polymer Chemical Resistance to Organic Solvents. https://arxiv.org/abs/2509.05344

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