Glass transition temperature, Tg, is an important thermophysical property of
polyacrylamides, which can be difficult to determine experimentally and resource-intensive …

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

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 the glass-transition temperatures (T g) of glass-forming polymers is of critical
importance as it governs the thermophysical properties of polymeric materials. The …

Synthetic polymers are versatile and widely used materials. Similar to small organic
molecules, a large chemical space of such materials is hypothetically accessible …

Data driven or machine learning (ML) based methods have been recently used in materials
science to provide quick material property predictions. Although powerful and robust, these …

Machine learning techniques are being applied in quantifying structure–property
relationships for a wide variety of materials, where the properly represented materials play …

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

There has been a tremendous increase in demand for virgin and postconsumer recycled
(PCR) polymers due to their wide range of chemical and physical characteristics. Despite …

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 …