Under the guidance of the material genome initiative (MGI), the use of data‐driven methods
to discover new materials has become an innovation of materials science. The polymer …

In the recent decades, with rapid development in computing power and algorithms, machine
learning (ML) has exhibited its enormous potential in new polymer discovery. Herein, the …

Polymer membranes perform innumerable separations with far-reaching environmental
implications. Despite decades of research, design of new membrane materials remains a …

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

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 …

Polymer materials are multiscale systems by definition. Already the description of a single
macromolecule involves a multitude of scales, and cooperative processes in polymer …

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 …

Developing amorphous polymers with desirable thermal conductivity has significant
implications, as they are ubiquitous in applications where thermal transport is critical …

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 …

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