In the last five years, there has been tremendous growth in machine learning and artificial
intelligence as applied to polymer science. Here, we highlight the unique challenges …

Polymers are ubiquitous to almost every aspect of modern society and their use in medical
products is similarly pervasive. Despite this, the diversity in commercial polymers used in …

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 …

Advances in machine learning (ML) and automated experimentation are poised to vastly
accelerate research in polymer science. Data representation is a critical aspect for enabling …

The emergence of data-intensive scientific discovery and machine learning has dramatically
changed the way in which scientists and engineers approach materials design …

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 …

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

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 …

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 …