Given the emergence of data science and machine learning throughout all aspects of
society, but particularly in the scientific domain, there is increased importance placed on …

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 …

Having a compact yet robust structurally based identifier or representation system is a key
enabling factor for efficient sharing and dissemination of research results within the …

Facilitating the application of machine learning (ML) to materials science problems requires
enhancing the data ecosystem to enable discovery and collection of data from many …

Scientific Machine Learning (SciML) and Artificial Intelligence (AI) will have broad use and
transformative effects across the Department of Energy. Accordingly, the January 2018 Basic …

A necessary transformation for a sustainable economy is the transition from fossil-derived
plastics to polymers derived from biomass and waste resources. While renewable …

Solid research depends on systematic, verifiable and repeatable scientometric analysis.
However, scientometric analysis is difficult in the current research landscape characterized …

The Block Copolymer Database (BCDB) is a platform that allows users to search, submit,
visualize, benchmark, and download experimental phase measurements and their …

The application of machine learning to the materials domain has traditionally struggled with
two major challenges: a lack of large, curated data sets and the need to understand the …