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 …

Polymers are diverse and versatile materials that have met a wide range of material
application demands. They come in several flavors and architectures (eg, homopolymers …

The use of machine learning (ML) models to screen new materials is becoming increasingly
common as they accelerate material discovery and increase sustainability. In this work, the …

Previous studies on glass-transition temperature (T g) prediction mainly focus on developing
diverse methods with higher regression accuracy, but very little attention has been paid to …

Artificial neural networks (ANNs) have been successfully used in the past to predict different
properties of polymers based on their chemical structure and to localize and quantify the …

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

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

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 …

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

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