A Model Ensemble Approach Enables Data-Driven Property Prediction for Chemically Deconstructable Thermosets in the Low-Data Regime

Yasmeen S. AlFaraj, Somesh Mohapatra, Peyton Shieh, Keith E.L. Husted, Douglass G. Ivanoff, Evan M. Lloyd, Julian C. Cooper, Yutong Dai, Avni P. Singhal, Jeffrey S. Moore, Nancy R. Sottos, Rafael Gomez-Bombarelli, Jeremiah A. Johnson

Research output: Contribution to journalArticlepeer-review

Abstract

Thermosets present sustainability challenges that could potentially be addressed through the design of deconstructable variants with tunable properties; however, the combinatorial space of possible thermoset molecular building blocks (e.g., monomers, cross-linkers, and additives) and manufacturing conditions is vast, and predictive knowledge for how combinations of these molecular components translate to bulk thermoset properties is lacking. Data science could overcome these problems, but computational methods are difficult to apply to multicomponent, amorphous, statistical copolymer materials for which little data exist. Here, leveraging a data set with 101 examples, we introduce a closed-loop experimental, machine learning (ML), and virtual screening strategy to enable predictions of the glass transition temperature (Tg) of polydicyclopentadiene (pDCPD) thermosets containing cleavable bifunctional silyl ether (BSE) comonomers and/or cross-linkers with varied compositions and loadings. Molecular features and formulation variables are used as model inputs, and uncertainty is quantified through model ensembling, which together with heavy regularization helps to avoid overfitting and ultimately achieves predictions within <15 °C for thermosets with compositionally diverse BSEs. This work offers a path to predicting the properties of thermosets based on their molecular building blocks, which may accelerate the discovery of promising plastics, rubbers, and composites with improved functionality and controlled deconstructability.

Original languageEnglish (US)
Pages (from-to)1810-1819
Number of pages10
JournalACS Central Science
Volume9
Issue number9
DOIs
StatePublished - Sep 27 2023

ASJC Scopus subject areas

  • General Chemistry
  • General Chemical Engineering

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