Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment
and accelerate research, hel** scientists to generate hypotheses, design experiments …

Traditional computational approaches to design chemical species are limited by the need to
compute properties for a vast number of candidates, eg, by discriminative modeling …

Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …

Artificial intelligence (AI)-based methods continue to make inroads into accelerated
materials design and development. Here, we review AI-enabled advances made in the …

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 …

The technological advances of the past century, marked by the computer revolution and the
advent of high-throughput screening technologies in drug discovery, opened the path to the …

Polymer networks are complex systems consisting of molecular components. Whereas the
properties of the individual components are typically well understood by most chemists …

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad
applications to challenging tasks in chemistry and materials science. Examples include the …

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 …

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 …