# AI-Assisted Design of Chemically Recyclable Polymers for Food Packaging

## TL;DR

This paper uses AI to design recyclable polymers for food packaging that are both sustainable and high-performing.

## Contribution

A polymer informatics workflow combining ML and virtual synthesis to identify and validate chemically recyclable polymers.

## Key findings

- AI screening identified thousands of promising chemically recyclable polymer candidates.
- Poly(p-dioxanone) was validated as a sustainable packaging material with strong barrier and recyclability properties.
- The workflow demonstrates a generalizable framework for sustainable polymer design under performance constraints.

## 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.