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 …

Prediction of chemical bioactivity and physical properties has been one of the most
important applications of statistical and more recently, machine learning and artificial …

Magnetic materials underpin modern technologies, ranging from data storage to energy
conversion to contactless sensing. However, the development of a new high-performance …

Artificial intelligence and machine learning are becoming indispensable tools in many areas
of physics, including astrophysics, particle physics, and climate science. In the arena of …

Samarium hexaboride (SmB6) is an example of a Kondo insulator, in which strong electron
correlations cause a band gap to open. SmB6 hosts both a bulk insulating state and a …

A priori prediction of phase stability of materials is a challenging practice, requiring
knowledge of all energetically competing structures at formation conditions. Large materials …

Computational materials discovery has been successful in predicting novel, technologically
relevant materials. However, it has remained focused almost exclusively on finding ground …

Machine learning (ML) is increasingly becoming a helpful tool in the search for novel
functional compounds. Here we use classification via random forests to predict the stability …

Motivated by the observation of light surface states in SmB 6, we examine the effects of
surface Kondo breakdown in topological Kondo insulators. We present both numerical and …

In 1928, Dirac proposed a wave equation to describe relativistic electrons. Shortly
afterwards, Klein solved a simple potential step problem for the Dirac equation and …