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 …

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 …

The structure of polymers determines their properties. Within the polymer structure,
substructures are often highly correlated with the corresponding properties. Due to the …

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

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 …

Machine learning offers promising tools to develop surrogate models for polymer structure-
property relations. Surrogate models can be built upon existing polymer data and are useful …

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 …