AI-assisted design of chemically recyclable polymers for food packaging

TL;DR

This study uses machine learning and virtual synthesis to identify and validate recyclable polymers for food packaging, demonstrating a new approach to sustainable material discovery with experimental confirmation of promising candidates.

Contribution

It introduces a polymer informatics workflow combining ML predictions and virtual synthesis to discover recyclable polymers, validated by experimental testing of poly(p-dioxanone).

Findings

Poly(p-dioxanone) has excellent water barrier and thermal properties.

Poly(p-dioxanone) can be chemically recycled with 95% monomer recovery.

The approach accelerates sustainable polymer discovery through computational screening.

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.