Prediction of the Glass-Transition Temperatures of Linear Homo/Heteropolymers and Cross-Linked Epoxy Resins

Chisa Higuchi, Dragos Horvath, Gilles Marcou, Kazunari Yoshizawa, Alexandre Varnek

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)


This work proposes a unified approach to predict glass-transition temperatures (Tg) of linear homo/heteropolymers and cross-linked epoxy resins by machine-learning approaches based on descriptors of reagents undergoing polymerization, represented in a formal way such as to encompass all three scenarios: linear homo- and heteropolymers plus network heteropolymers. The "formal"representation of reagents is a problem-specific, herein designed standardization protocol of compounds, unlike typical structure curation rules in chemoinformatics. For example, heteropolymers are represented by the two partner reagents, whereas homopolymers are depicted as formal "heteropolymers"with identical partners. The key rule proposed here is to choose "formal"monomers such as to minimize the number of marked atoms, involved in bonds being formed or changing bond order. Accordingly, carbonyl compounds are rendered as the less-stable vinyl alcohol tautomer, following the same formalism as in olefin polymerization, to minimize the total number of formal polymerization mechanisms and herewith provide the most general framework encompassing a maximum of polymerization processes. ISIDA (in silico design and data analysis) fragment counts with special status given to the "marked atoms"participating in the polymerization process were combined using "mixture"strategies to generate the final polymer descriptors. Three predictive models based on SVR (support vector regression) are discussed here. After reproducing results of Katritzky et al. with a local model applicable only to linear homo/heteropolymers, an epoxy resin-specific model applicable to both linear and network forms was built. Eventually, the general model applicable to all these families was constructed. In 12 × repeated 3-fold cross-validation challenges, it displayed the highest accuracy of Q2 = 0.920, RMSE = 34.3 K over the training set of 270 polymers, and R2 = 0.779, RMSE 35.9 K for an external test set of 119 polymers. GTM (Generative Topographic Mapping) analysis produced a 2D map of "polymer chemical space", highlighting the various classes of polymers included in the study and their relationship with respect to Tg values. The epoxy-specific and general models are publicly available on our web server:

Original languageEnglish
Pages (from-to)1430-1442
Number of pages13
JournalACS Applied Polymer Materials
Issue number6
Publication statusPublished - Jun 14 2019

All Science Journal Classification (ASJC) codes

  • Polymers and Plastics
  • Process Chemistry and Technology
  • Organic Chemistry


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