From Quantum Mechanics to Coarse-Grained Models: Bridging the Gap toward Polymer Rational Design
design of novel polymers. Subjected to further automation, the procedure could also be integrated into computational machine-learning-based high-throughput schemes, paving the way toward an efficient data-driven polymer discovery.
Authors
- Serena Coiai (ORCID: https://orcid.org/0000-0002-9333-8152)
- Alessandro Mossa (ORCID: https://orcid.org/0000-0002-1589-8920)
- Giacomo Prampolini (ORCID: https://orcid.org/0000-0002-0547-8893)
- Giorgia Brancolini (ORCID: https://orcid.org/0000-0002-1083-9198)
- Andoni Ugartemendia (ORCID: https://orcid.org/0000-0003-4091-7780)
- Abderrahmane Semmeq
Institutions
- Istituto Nanoscienze (IT)
- Institute for the Chemistry of OrganoMetallic Compounds (IT)
- Donostia International Physics Center (ES)
Publication Details
- Journal
- Journal of Chemical Theory and Computation
- Published
- 2026-04-01
- DOI
- https://doi.org/10.1021/acs.jctc.5c02092
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Euskal Herriko Unibertsitatea