Graph-Based Machine Learning Identifies Oxygenated Block Polymer Replacements for Conventional Plastics and Elastics
Soheila Molaei, Kam C. Poon, Chang Gao, Katharina H. S. Eisenhardt, Matilde Concilio, Gregory S. Sulley, David Kohan Marzagão, Georgina L. Gregory, David A. Clifton, Clive R. Siviour, Charlotte K. Williams
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.
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…
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Taxonomy
TopicsMachine Learning in Materials Science · Polymer crystallization and properties · Advanced Polymer Synthesis and Characterization