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

The advent of big data and algorithmic developments in the field of machine learning (and
artificial intelligence, in general) have greatly impacted the entire spectrum of physical …

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

Synthetic polymers are omnipresent in society as textiles and packaging materials, in
construction and medicine, among many other important applications. Alternatively, natural …

Polymer–protein hybrids are intriguing materials that can bolster protein stability in non‐
native environments, thereby enhancing their utility in diverse medicinal, commercial, and …

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 …

Polymers are a vital part of everyday life. Their chemical universe is so large that it presents
unprecedented opportunities as well as significant challenges to identify suitable application …

Rationale is defined as a subset of input features that best explains or supports the
prediction by machine learning models. Rationale identification has improved the …

Modern polymer science suffers from the curse of multidimensionality. The large chemical
space imposed by including combinations of monomers into a statistical copolymer …

Accurate and efficient prediction of polymer properties is of great significance in polymer
design. Conventionally, expensive and time-consuming experiments or simulations are …