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 …

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 …

Owing to the rapid developments to improve the accuracy and efficiency of both
experimental and computational investigative methodologies, the massive amounts of data …

Artificial intelligence (AI) and machine learning (ML) have been increasingly used in
materials science to build predictive models and accelerate discovery. For selected …

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 …

Materials science and chemistry have played a central and significant role in advancing
society. With the shift toward sustainable living, it is anticipated that the development of …

High-throughput data generation methods and machine learning (ML) algorithms have
given rise to a new era of computational materials science by learning the relations between …

Lithium-ion batteries play a pivotal role in a wide range of applications, from electronic
devices to large-scale electrified transportation systems and grid-scale energy storage …

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