at Google ScholarTitle data
Shukla, Shivank S. ; Künneth, Christopher ; Ramprasad, Rampi:
Polymer Informatics Beyond Homopolymers.
arXiv
,
2023
DOI: https://doi.org/10.48550/arXiv.2303.12938
Abstract in another language
Polymers are diverse and versatile materials that have met a wide range of material application demands. They come in several flavors and architectures, e.g., homopolymers, copolymers, polymer blends, and polymers with additives. Searching this enormous space for suitable materials with a specific set of property/performance targets is thus non-trivial, painstaking, and expensive. Such a search process can be made effective by the creation of rapid and accurate property predictors. In this work, we present a machine-learning framework to predict the thermal properties of homopolymers, copolymers, and polymer blends. A universal fingerprinting scheme capable of handling this entire polymer chemical class has been developed and a multi-task deep learning algorithm is trained simultaneously on a large dataset of glass transition, melting, and degradation temperatures. The developed models are accurate, fast, flexible, and scalable to other properties when suitable data become available.
Further data
| Item Type: | Preprint, postprint |
|---|---|
| Institutions of the University: | Faculties > Faculty of Engineering Science > Junior Professor Computational Materials Science > Junior Professor Computational Materials Science - Juniorprof. Dr. Christopher Künneth Faculties Faculties > Faculty of Engineering Science Faculties > Faculty of Engineering Science > Junior Professor Computational Materials Science |
| Result of work at the UBT: | No |
| DDC Subjects: | 600 Technology, medicine, applied sciences > 620 Engineering |
| Date Deposited: | 05 May 2023 08:44 |
| Last Modified: | 23 Aug 2023 11:10 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/76177 |