This review discussed the dilemma of small data faced by materials machine learning. First,
we analyzed the limitations brought by small data. Then, the workflow of materials machine …

As materials researchers increasingly embrace machine-learning (ML) methods, it is natural
to wonder what lessons can be learned from other fields undergoing similar developments …

Polymer membranes perform innumerable separations with far-reaching environmental
implications. Despite decades of research, design of new membrane materials remains a …

Materials with ultrahigh or low thermal conductivity are desirable for many technological
applications, such as thermal management of electronic and photonic devices, heat …

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 modular nature of metal–organic frameworks (MOFs) enables synthetic control over
their physical and chemical properties, but it can be difficult to know which MOFs would be …

Artificial intelligence (AI) based approaches are beginning to impact several domains of
human life, science and technology. Polymer informatics is one such domain where AI and …

There is a growing demand for the use of machine learning (ML) to derive fast-to-evaluate
surrogate models of materials properties. In recent years, a broad array of materials property …

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

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 …