# Graph-Based Machine Learning Identifies Oxygenated Block Polymer Replacements for Conventional Plastics and Elastics

## TL;DR

This paper introduces a machine learning method to design sustainable oxygenated block polymers that can replace conventional plastics and elastics.

## Contribution

A novel graph-based machine learning approach called PolyReco for predicting high-performance oxygenated block polymer structures.

## Key findings

- PolyReco successfully identifies oxygenated block polymers with mechanical properties comparable to conventional plastics and elastomers.
- Experimental validation confirms the predictive power of PolyReco in creating sustainable polymer alternatives.
- The method supports the transition to a circular plastics economy by reducing reliance on fossil-based materials.

## Abstract

Oxygenated block polymers, comprising esters and carbonates,
are
priority materials to replace petrochemical polymers in a circular
plastics economy. These materials should repopulate the thermomechanical
property space mapped by current plastics and elastomers. Here, a
novel machine learning approach, PolyReco, predicts structures of
oxygenated block polymers meeting the mechanical performance thresholds
for widely used and hard-to-replace petroleum derived hydrocarbon
polymers. Triblock oxygenated polymers are represented as graphs,
and a link prediction algorithm enables feature extraction to identify
new block polymer combinations, and associated degrees of polymerization,
to meet the target properties. PolyReco is paired with a visualization
tool for further material down selection based on user requirements.
Three case studies highlight and experimentally validate its predictive
power for identifying high-performance oxygenated block polymers,
with new block polymers prepared and tested. These new block polymers
exhibit tensile mechanical properties in the range of high-impact
polystyrene, poly­(dimethylsiloxane), and styrenic elastomers; the
experimental results indicate that PolyReco may help support the identification
of sustainable materials that could reduce dependence on fossil-based
polymer incumbents.