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

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 …

Abstract Machine learning (ML) offers promising tools to develop surrogate models for
polymers' structure–property relations. Surrogate models can be built upon existing polymer …

Technological advances and the need for new polymers necessitate continuous research in
the design and identification of polymers with specific physical and chemical properties …

To formulate a machine learning (ML) model to establish the polymer's structure-property
correlation for glass transition temperature T g, we collect a diverse set of nearly 13,000 real …

In this work convolutional-fully connected neural networks were designed and trained to
predict the glass transition temperature of polymers based only on their chemical structure …

Quantitative structure− property relationships (QSPR) are developed to correlate glass
transition temperatures and chemical structure. Both monomer and repeat unit structures are …

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 …

Melting point prediction for organic molecules has drawn widespread attention from both
academic and industrial communities. In this work, a learnable graph neural fingerprint …

Glass transition temperature, Tg, is an important thermophysical property of
polyacrylamides, which can be difficult to determine experimentally and resource-intensive …