at Google ScholarTitle data
Künneth, Christopher ; Schertzer, William ; Ramprasad, Rampi:
Copolymer Informatics with Multitask Deep Neural Networks.
In: Macromolecules.
Vol. 54
(2021)
Issue 13
.
- pp. 5957-5961.
ISSN 1520-5835
DOI: https://doi.org/10.1021/acs.macromol.1c00728
Abstract in another language
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.
Further data
| Item Type: | Article in a journal |
|---|---|
| Refereed: | Yes |
| Additional notes: | Correction:
https://doi.org/10.1021/acs.macromol.1c01539 |
| Institutions of the University: | Faculties > Faculty of Engineering Science > Juniorprofessur Computational Materials Science > Juniorprofessur Computational Materials Science - Juniorprof. Dr. Christopher Künneth Faculties Faculties > Faculty of Engineering Science Faculties > Faculty of Engineering Science > Juniorprofessur Computational Materials Science |
| Result of work at the UBT: | No |
| DDC Subjects: | 600 Technology, medicine, applied sciences > 620 Engineering |
| Date Deposited: | 05 May 2023 08:45 |
| Last Modified: | 05 May 2023 08:45 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/76155 |