Artificial intelligence-based methods are becoming increasingly effective at screening
libraries of polymers down to a selection that is manageable for experimental inquiry. The …

Modern data-driven tools are transforming application-specific polymer development cycles.
Surrogate models that can be trained to predict properties of polymers are becoming …

The design of new functional polymers depends on the successful navigation of their
structure-function landscapes. Advances in combinatorial polymer chemistry and machine …

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

Accurately predicting new polymers' properties with machine learning models apriori to
synthesis has potential to significantly accelerate new polymers' discovery and …

Machine learning methods have shown promise in predicting molecular properties, and
given sufficient training data, machine learning approaches can enable rapid high …

Polymers are incredibly versatile materials and have become ubiquitous. Increasingly,
researchers are using data science and polymer informatics to design new materials and …

Optimal design of polymers is a challenging task due to their enormous chemical and
configurational space. Recent advances in computations, machine learning, and increasing …

In this perspective, the authors challenge the status quo of polymer innovation. The authors
first explore how research in polymer design is conducted today, which is both time …

There has been rapidly growing demand of polymeric materials coming from different
aspects of modern life because of the highly diverse physical and chemical properties of …