bei Google ScholarTitelangaben
Künneth, Christopher ; Schertzer, William ; Ramprasad, Rampi:
Copolymer Informatics with Multitask Deep Neural Networks.
In: Macromolecules.
Bd. 54
(2021)
Heft 13
.
- S. 5957-5961.
ISSN 1520-5835
DOI: https://doi.org/10.1021/acs.macromol.1c00728
Abstract
Polymer informatics tools have been recently gaining ground to efficiently and effectively develop, design, and discover new polymers that meet specific application needs. So far, however, these data-driven efforts have largely focused on homopolymers. Here, we address the property prediction challenge for copolymers, extending the polymer informatics framework beyond homopolymers. Advanced polymer fingerprinting and deep-learning schemes that incorporate multitask learning and meta learning are proposed. A large data set containing over 18 000 data points of glass transition, melting, and degradation temperature of homopolymers and copolymers of up to two monomers is used to demonstrate the copolymer prediction efficacy. The developed models are accurate, fast, flexible, and scalable to more copolymer properties when suitable data become available.
Weitere Angaben
| Publikationsform: | Artikel in einer Zeitschrift |
|---|---|
| Begutachteter Beitrag: | Ja |
| Zusätzliche Informationen: | Correction:
https://doi.org/10.1021/acs.macromol.1c01539 |
| Institutionen der Universität: | Fakultäten > Fakultät für Ingenieurwissenschaften > Juniorprofessur Computational Materials Science > Juniorprofessur Computational Materials Science - Juniorprof. Dr. Christopher Künneth Fakultäten Fakultäten > Fakultät für Ingenieurwissenschaften Fakultäten > Fakultät für Ingenieurwissenschaften > Juniorprofessur Computational Materials Science |
| Titel an der UBT entstanden: | Nein |
| Themengebiete aus DDC: | 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften |
| Eingestellt am: | 05 Mai 2023 08:45 |
| Letzte Änderung: | 05 Mai 2023 08:45 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/76155 |