DOE OSTI · 2514555
Predicting homopolymer and copolymer solubility through machine learning
Abstract
In this work, we report the development of multiple new machine learning (ML) models to accurately predict homopolymer/copolymer solubility over a diverse set of polymers & solvents, using explainable AI to provide polymer design recommendations.
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Stubbs, Christopher D. [Department of Chemistry, Colorado State University, Fort Collins, CO 80523-1872, USA] (ORCID:0000000301273417), Kim, Yeonjoon [Department of Chemistry, Pukyong National University, Busan, Republic of Korea] (ORCID:0000000247847925), Quinn, Ethan C. [Department of Chemistry, Colorado State University, Fort Collins, CO 80523-1872, USA] (ORCID:000000029609806X), Pérez-Soto, Raúl [Department of Chemistry, Colorado State University, Fort Collins, CO 80523-1872, USA] (ORCID:0000000262372155), Chen, Eugene Y. -X. [Department of Chemistry, Colorado State University, Fort Collins, CO 80523-1872, USA] (ORCID:0000000175123484), Kim, Seonah [Department of Chemistry, Colorado State University, Fort Collins, CO 80523-1872, USA] (ORCID:0000000198467140). 2025-02-12. Predicting homopolymer and copolymer solubility through machine learning. https://doi.org/10.1039/d4dd00290c
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