One of the most exciting tools that have entered the material science toolbox in recent years
is machine learning. This collection of statistical methods has already proved to be capable …

Solid-state batteries have fascinated the research community over the past decade, largely
due to their improved safety properties and potential for high-energy density. Searching for …

Predicting the stability of the perovskite structure remains a long-standing challenge for the
discovery of new functional materials for many applications including photovoltaics and …

Recent advances in experimental and computational methods are increasing the quantity
and complexity of generated data. This massive amount of raw data needs to be stored and …

The development of materials is one of the driving forces to accelerate modern scientific
progress and technological innovation. Machine learning (ML) technology is rapidly …

Abstract Machine learning plays an important role in accelerating the discovery and design
process for novel electrochemical energy storage materials. This review aims to provide the …

Rapidly discovering functional materials remains an open challenge because the traditional
trial-and-error methods are usually inefficient especially when thousands of candidates are …

Carbon capture, utilisation and storage (CCUS) will play a critical role in future
decarbonisation efforts to meet the Paris Agreement targets and mitigate the worst effects of …

There is growing interest in applying machine learning techniques in the research of
materials science. However, although it is recognized that materials datasets are typically …

By combining metal nodes with organic linkers we can potentially synthesize millions of
possible metal–organic frameworks (MOFs). The fact that we have so many materials opens …