Quantitative structure-property relationship (QSPR) is a powerful analytical method to find
correlations between the structure of a molecule and its physicochemical properties. The …

Artificial neural networks (ANNs), a subset of Quantitative Structure-Property Relationship
(QSPR) methods, offer a promising avenue for addressing challenges in materials science …

Glass transition temperature (T g) is important for understanding the physical and
mechanical properties of a polymer material because it relates to the thermal energy …

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 …

Quantitative structure− property relationships (QSPR) are developed to correlate glass
transition temperatures and chemical structure. Both monomer and repeat unit structures are …

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 …

Predicting binary mixtures' glass transition temperature (T g) is crucial in various fields,
particularly for industrial materials affected by this property during production processes and …

This research introduces an advanced data-centric framework tailored for polymer design.
Developing a machine learning (ML) model utilizing molecular descriptors, the study aims to …

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

Glasses offer a broad range of tunable thermophysical properties that are linked to their
compositions. However, it is challenging to establish a universal composition-property …