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 …

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 …

Finding amorphous polymers with higher thermal conductivity is important, as they are
ubiquitous in a wide range of applications where heat transfer is important. With recent …

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 …

Polyhydroxyalkanoate-based polymers—being ecofriendly, biosynthesizable, and
economically viable and possessing a broad range of tunable properties—are currently …

The ability to efficiently design new and advanced polymers with functional thermal
properties is hampered by the high-cost and time-consuming experiments. Machine learning …

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 …

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

The use of machine learning in computational molecular design has great potential to
accelerate the discovery of innovative materials. However, its practical benefits still remain …