Predicting homopolymer and copolymer solubility through machine learning
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.
Authors
- Yeonjoon Kim (ORCID: https://orcid.org/0000-0002-4784-7925)
- Ethan C. Quinn (ORCID: https://orcid.org/0000-0002-9609-806X)
- Raúl Pérez‐Soto (ORCID: https://orcid.org/0000-0002-6237-2155)
- Eugene Y.‐X. Chen (ORCID: https://orcid.org/0000-0001-7512-3484)
- Seonah Kim (ORCID: https://orcid.org/0000-0001-9846-7140)
- Christopher D Stubbs (ORCID: https://orcid.org/0000-0003-0127-3417)
Institutions
- Pukyong National University (KR)
- Colorado State University (US)
Publication Details
- Journal
- Digital Discovery
- Published
- 2024-12-23
- DOI
- https://doi.org/10.1039/d4dd00290c
- Citations
- 5
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.59
Funders
- National Science Foundation
- U.S. Department of Energy
- Colorado State University
- Division of Chemistry
- Office of Energy Efficiency