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 …

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

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 …

Polymer informatics tools have been recently gaining ground to efficiently and effectively
develop, design, and discover new polymers that meet specific application needs. So far …

Over the past decade, there has been a resurgence in the importance of data-driven
techniques in materials science and engineering. The utilization of state-of-the art …

In the present work, we address the problem of utilizing machine learning (ML) methods to
predict the thermal properties of polymers by establishing “structure–property” relationships …

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 …

Polymers have been widely used in energy storage, construction, medicine, aerospace, and
so on. However, the complexity of chemical composition and morphology of polymers has …

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