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Polymer Informatics at Scale with Multitask Graph Neural Networks

Title data

Gurnani, Rishi ; Künneth, Christopher ; Toland, Aubrey ; Ramprasad, Rampi:
Polymer Informatics at Scale with Multitask Graph Neural Networks.
In: Chemistry of Materials. Vol. 35 (2023) Issue 4 . - pp. 1560-1567.
ISSN 1520-5002
DOI: https://doi.org/10.1021/acs.chemmater.2c02991

Abstract in another language

Artificial intelligence-based methods are becoming increasingly effective at screening libraries of polymers down to a selection that is manageable for experimental inquiry. The vast majority of presently adopted approaches for polymer screening rely on handcrafted chemostructural features extracted from polymer repeat units─a burdensome task as polymer libraries, which approximate the polymer chemical search space, progressively grow over time. Here, we demonstrate that directly “machine learning” important features from a polymer repeat unit is a cheap and viable alternative to extracting expensive features by hand. Our approach─based on graph neural networks, multitask learning, and other advanced deep learning techniques─speeds up feature extraction by 1–2 orders of magnitude relative to presently adopted handcrafted methods without compromising model accuracy for a variety of polymer property prediction tasks. We anticipate that our approach, which unlocks the screening of truly massive polymer libraries at scale, will enable more sophisticated and large scale screening technologies in the field of polymer informatics.

Further data

Item Type: Article in a journal
Refereed: Yes
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 08:43
Last Modified: 05 May 2023 08:43
URI: https://eref.uni-bayreuth.de/id/eprint/76178