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Bioplastic design using multitask deep neural networks

Titelangaben

Künneth, Christopher ; Lalonde, Jessica ; Marrone, Babetta L. ; Iverson, Carl N. ; Ramprasad, Rampi ; Pilania, Ghanshyam:
Bioplastic design using multitask deep neural networks.
In: Communications Materials. Bd. 3 (2022) Heft 1 . - 96.
ISSN 2662-4443
DOI: https://doi.org/10.1038/s43246-022-00319-2

Abstract

Non-degradable plastic waste jeopardizes our environment, yet our modern lifestyle and current technologies are impossible to sustain without plastics. Bio-synthesized and biodegradable alternatives such as polyhydroxyalkanoates (PHAs) have the potential to replace large portions of the world’s plastic supply with cradle-to-cradle materials, but their chemical complexity and diversity limit traditional resource-intensive experimentation. Here, we develop multitask deep neural network property predictors using available experimental data for a diverse set of nearly 23,000 homo- and copolymer chemistries. Using the predictors, we identify 14 PHA-based bioplastics from a search space of almost 1.4 million candidates which could serve as potential replacements for seven petroleum-based commodity plastics that account for 75% of the world’s yearly plastic production. We also discuss possible synthesis routes for the identified promising materials.

Weitere Angaben

Publikationsform: Artikel in einer Zeitschrift
Begutachteter Beitrag: Ja
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 08:48
Letzte Änderung: 05 Mai 2023 08:48
URI: https://eref.uni-bayreuth.de/id/eprint/76169