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Künneth, Christopher ; Rajan, Arunkumar Chitteth ; Tran, Huan ; Chen, Lihua ; Kim, Chiho ; Ramprasad, Rampi:
Polymer informatics with multi-task learning.
In: Patterns.
Bd. 2
(2021)
Heft 4
.
- 100238.
ISSN 2666-3899
DOI: https://doi.org/10.1016/j.patter.2021.100238
Abstract
Modern data-driven tools are transforming application-specific polymer development cycles. Surrogate models that can be trained to predict properties of polymers are becoming commonplace. Nevertheless, these models do not utilize the full breadth of the knowledge available in datasets, which are oftentimes sparse; inherent correlations between different property datasets are disregarded. Here, we demonstrate the potency of multi-task learning approaches that exploit such inherent correlations effectively. Data pertaining to 36 different properties of over 13,000 polymers are supplied to deep-learning multi-task architectures. Compared to conventional single-task learning models, the multi-task approach is accurate, efficient, scalable, and amenable to transfer learning as more data on the same or different properties become available. Moreover, these models are interpretable. Chemical rules, that explain how certain features control trends in property values, emerge from the present work, paving the way for the rational design of application specific polymers meeting desired property or performance objectives.
Weitere Angaben
| Publikationsform: | Artikel in einer Zeitschrift |
|---|---|
| Begutachteter Beitrag: | Ja |
| Keywords: | polymer informatics; polymer property of prediction; polymer design; multi-task; machine learning; neural network; Gaussian processing; data-driven methods |
| Institutionen der Universität: | Fakultäten > Fakultät für Ingenieurwissenschaften > Juniorprofessur Computational Materials Science > Juniorprofessur Computational Materials Science - Juniorprof. Dr. Christopher Künneth |
| Titel an der UBT entstanden: | Nein |
| Themengebiete aus DDC: | 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften |
| Eingestellt am: | 05 Mai 2023 08:52 |
| Letzte Änderung: | 05 Mai 2023 08:52 |
| URI: | https://eref.uni-bayreuth.de/id/eprint/76154 |