AI-Assisted Design of Chemically Recyclable Polymers for Food Packaging.
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| Title: | AI-Assisted Design of Chemically Recyclable Polymers for Food Packaging. |
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| Authors: | Phan, Brandon K.1 (AUTHOR), Kim, Chiho1,2 (AUTHOR), Nistane, Janhavi1 (AUTHOR), Xiong, Wei1,2 (AUTHOR), Chen, Haoyu2 (AUTHOR), Jang, Woo Jin2 (AUTHOR), Gholami, Farzad1 (AUTHOR), Su, Yongliang2 (AUTHOR), Qi, Jerry1 (AUTHOR), Lively, Ryan2 (AUTHOR), Gutekunst, Will2 (AUTHOR), Ramprasad, Rampi1 (AUTHOR) rampi.ramprasad@mse.gatech.edu |
| Source: | Polymers (20734360). Mar2026, Vol. 18 Issue 6, p730. 14p. |
| Subjects: | Food packaging, Ring-opening polymerization, Waste recycling, Recyclable material, Polymers, Polymer structure |
| Abstract: | Polymer packaging plays a crucial role in food preservation but poses major challenges in recycling and environmental persistence. To address the need for sustainable, high-performance alternatives, we employed a polymer informatics workflow to identify single- and multi-layer drop-in replacements for polymer-based packaging materials. Machine learning (ML) models, trained on carefully curated polymer datasets, predicted eight key properties across a library of approximately 7.4 million ring-opening polymerization (ROP) polymers generated by virtual forward synthesis (VFS). Candidates were prioritized by the enthalpy of polymerization, a critical metric for chemical recyclability. This screening yielded thousands of promising candidates, demonstrating the feasibility of replacing diverse packaging architectures. We then experimentally validated poly(p-dioxanone) (poly-PDO), an existing ROP polymer whose barrier performance had not been previously reported. Validation showed that poly-PDO exhibits strong water barrier performance, mechanical and thermal properties consistent with predictions, and excellent chemical recyclability (∼95% monomer recovery), thereby meeting the design targets and underscoring its potential for sustainable packaging. These findings highlight the power of informatics-driven approaches to accelerate the discovery of sustainable polymers by uncovering opportunities in both existing and novel chemistries. Beyond identifying potential replacements, this work establishes a generalizable framework for navigating vast polymer design spaces under competing performance constraints. The results illustrate how data-driven polymer design can bridge the gap between sustainability concepts and experimentally realizable materials for real-world packaging applications. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Polymer packaging plays a crucial role in food preservation but poses major challenges in recycling and environmental persistence. To address the need for sustainable, high-performance alternatives, we employed a polymer informatics workflow to identify single- and multi-layer drop-in replacements for polymer-based packaging materials. Machine learning (ML) models, trained on carefully curated polymer datasets, predicted eight key properties across a library of approximately 7.4 million ring-opening polymerization (ROP) polymers generated by virtual forward synthesis (VFS). Candidates were prioritized by the enthalpy of polymerization, a critical metric for chemical recyclability. This screening yielded thousands of promising candidates, demonstrating the feasibility of replacing diverse packaging architectures. We then experimentally validated poly(p-dioxanone) (poly-PDO), an existing ROP polymer whose barrier performance had not been previously reported. Validation showed that poly-PDO exhibits strong water barrier performance, mechanical and thermal properties consistent with predictions, and excellent chemical recyclability (∼95% monomer recovery), thereby meeting the design targets and underscoring its potential for sustainable packaging. These findings highlight the power of informatics-driven approaches to accelerate the discovery of sustainable polymers by uncovering opportunities in both existing and novel chemistries. Beyond identifying potential replacements, this work establishes a generalizable framework for navigating vast polymer design spaces under competing performance constraints. The results illustrate how data-driven polymer design can bridge the gap between sustainability concepts and experimentally realizable materials for real-world packaging applications. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20734360 |
| DOI: | 10.3390/polym18060730 |
