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 …

Polyimide substrates used in flexible display devices need to withstand very high
temperatures and be highly thermally stable. The discovery of polyimides that satisfy these …

Polymers with exceptional heat resistance are critically valuable in numerous domains,
particularly as essential components of flexible organic light-emitting diodes. Among these …

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 …

Establishing the structure–property relationship by machine learning (ML) models is
extremely valuable for accelerating the molecular design of polymers. However, existing ML …

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

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 present machine learning models for the prediction of thermal and mechanical
properties of polymers based on the graph convolutional network (GCN). GCN-based …

The prediction of a variety of polymer properties from their monomer composition has been a
challenge for material informatics, and their development can lead to a more effective …

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 …