BibTex format
@article{Molaei:2026:10.1021/jacs.5c21416,
author = {Molaei, S and Poon, KC and Gao, C and Eisenhardt, KHS and Concilio, M and Sulley, GS and Marzagão, DK and Gregory, GL and Clifton, DA and Siviour, CR and Williams, CK},
doi = {10.1021/jacs.5c21416},
journal = {J Am Chem Soc},
pages = {10934--10944},
title = {Graph-Based Machine Learning Identifies Oxygenated Block Polymer Replacements for Conventional Plastics and Elastics.},
url = {http://dx.doi.org/10.1021/jacs.5c21416},
volume = {148},
year = {2026}
}
RIS format (EndNote, RefMan)
TY - JOUR
AB - 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.
AU - Molaei,S
AU - Poon,KC
AU - Gao,C
AU - Eisenhardt,KHS
AU - Concilio,M
AU - Sulley,GS
AU - Marzagão,DK
AU - Gregory,GL
AU - Clifton,DA
AU - Siviour,CR
AU - Williams,CK
DO - 10.1021/jacs.5c21416
EP - 10944
PY - 2026///
SP - 10934
TI - Graph-Based Machine Learning Identifies Oxygenated Block Polymer Replacements for Conventional Plastics and Elastics.
T2 - J Am Chem Soc
UR - http://dx.doi.org/10.1021/jacs.5c21416
UR - https://www.ncbi.nlm.nih.gov/pubmed/41781193
VL - 148
ER -