Nature has only provided us with a limited number of biobased and biodegradable building
blocks. Therefore, the fine-tuning of the sustainable polymer properties is expected to be …

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 …

The success of the Materials Genome Initiative has led to opportunities for data-driven
approaches for materials discovery. The recent development of Polymer Genome (PG) …

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 …

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 …

Recent advances in graphics processing unit (GPU) hardware and improved efficiencies of
atomistic simulation programs allow for the screening of a large number of polymers to …

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 …

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 …

Molecular dynamics simulations of polymer samples with all-atom resolution provide
important insight into the relationship between the atom-level structure and macroscopic …

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