Alloy modelling has a history of machine-learning-like approaches, preceding the tide of
data-science-inspired work. The dawn of computational databases has made the integration …

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 …

Abstract Machine learning (ML) is rapidly revolutionizing many fields and is starting to
change landscapes for physics and chemistry. With its ability to solve complex tasks …

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 …

Two hydrogen-rich materials, H 3 S and LaH 10, synthesized at megabar pressures, have
revolutionized the field of condensed matter physics providing the first glimpse to the …

Superconductivity has been the focus of enormous research effort since its discovery more
than a century ago. Yet, some features of this unique phenomenon remain poorly …

We estimate a statistical model to predict the superconducting critical temperature based on
the features extracted from the superconductor's chemical formula. The statistical model …

Abstract Machine learning (ML) is widely used to uncover structure–property relationships of
materials due to its ability to quickly find potential data patterns and make accurate …

More than a century after the discovery of superconductors (SCs), numerous studies have
been accomplished to take advantage of SCs in physics, power engineering, quantum …

The high-temperature superconductor, YBa 2 Cu 3 O 7-x (YBCO), is a promising candidate
for high field magnet fabrication as it has critical temperature, T c, of over 80 K and an upper …