The glass transition temperature (Tg) is one of the most important properties affecting the
stability of a polymeric material. A cheminformatics‐based approach has been employed to …

This work proposes a unified approach to predict glass-transition temperatures (T g) of linear
homo/heteropolymers and cross-linked epoxy resins by machine-learning approaches …

In the field of polymer informatics, utilizing machine learning (ML) techniques to evaluate the
glass transition temperature T g and other properties of polymers has attracted extensive …

The case of sustainable solvents is of great interest both academically and industrially. With
research communities becoming more aware of the negative impacts of conventional …

We propose a chemical language processing model to predict polymers' glass transition
temperature (T g) through a polymer language (SMILES, Simplified Molecular Input Line …

Predicting the glass-transition temperatures (T g) of glass-forming polymers is of critical
importance as it governs the thermophysical properties of polymeric materials. The …

In this study, a new neural network quantitative structure-property relationship model for
prediction of θ (LCST) of polymer solutions is presented. The parameters of this model are …

In the present work, a quantitative structure− property relationship (QSPR) study is
performed to predict the aniline point temperature of pure hydrocarbon components. As a …

In this study, the support vector machine (SVM), as a novel type of learning machine, for the
first time, was used to construct a quantitative structure-property relationship model for the …

A recursive neural network QSPR model that can take directly molecular structures as input
was applied to the prediction of the glass transition temperature of 277 poly (meth) acrylates …