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Designing formulations of bio-based, multicomponent epoxy resin systems via machine learning

Title data

Albuquerque, Rodrigo Q. ; Rothenhäusler, Florian ; Ruckdäschel, Holger:
Designing formulations of bio-based, multicomponent epoxy resin systems via machine learning.
In: MRS Bulletin. (30 June 2023) .
ISSN 1938-1425
DOI: https://doi.org/10.1557/s43577-023-00504-9

Official URL: Volltext

Project information

Project financing: Bayerisches Staatsministerium für Wissenschaft und Kunst
Bundesministerium für Wirtschaft und Klimaschutz

Abstract in another language

Petroleum-based epoxy resins are commonly used as a matrix in fiberreinforced polymer composites. Bio-based epoxy resin systems could be a more environmentally friendly alternative to conventional epoxy resins. In this work, novel
formulations of multicomponent, amino acid-based resin systems exhibiting high or low glass-transition temperatures (Tg) were designed via Bayesian optimization and
active learning techniques. After only five high-Tg experiments, thermosets with Tg already higher than those of the individual components were obtained, pointing out
the existence of synergistic effects among the amino acids used and confirming the efficiency of the theoretical design. Linear and nonlinear machine learning (ML)
models successfully predicted Tg with a mean absolute error of 3.98◦C and R² score of 0.91. A price reduction of up to 13.7% was achieved while maintaining the Tg of 130◦C using an optimized formulation. The LASSO model provided
information about the dependence of Tg on the number of active hydrogen atoms and aromaticity. This study highlights the importance of Bayesian optimization and ML to achieve a more sustainable development of epoxy resin materials.

Further data

Item Type: Article in a journal
Refereed: Yes
Institutions of the University: Faculties > Faculty of Engineering Science > Chair Polymer Materials > Chair Polymer Materials - Univ.-Prof. Dr.-Ing. Holger Ruckdäschel
Faculties
Faculties > Faculty of Engineering Science
Faculties > Faculty of Engineering Science > Chair Polymer Materials
Result of work at the UBT: Yes
DDC Subjects: 600 Technology, medicine, applied sciences > 620 Engineering
Date Deposited: 13 Jan 2024 22:00
Last Modified: 15 Jan 2024 09:06
URI: https://eref.uni-bayreuth.de/id/eprint/88207