The chemical space for designing materials is practically infinite. This makes disruptive
progress by traditional physics-based modeling alone challenging. Yet, training data for …

Maximization of the configurational entropy–this has been the magic formula promising
thousands or even millions of intermetallic multiple-principal-element solid solutions with …

The prediction of the phase formation of high entropy alloys (HEAs) has attracted great
research interest recent years due to their superior structure and mechanical properties of …

The empirical rules for the prediction of solid solution formation proposed so far in the
literature usually have very compromised predictability. Some rules with seemingly good …

High-entropy alloys have exhibited unusual materials properties. The stability of equimolar
single-phase solid solution of five or more elements is supposedly rare and identifying the …

Abstract Machine learning (ML) has emerged as a powerful tool in the research field of high
entropy compounds (HECs), which have gained worldwide attention due to their vast …

We develop a fast and accurate machine-learned interatomic potential for the Mo-Nb-Ta-VW
quinary system and use it to study segregation and defects in the body-centered-cubic …

Chemical short-range order (CSRO) in high entropy alloys (HEAs) has attracted interests
recently and is believed to be capable for tuning their mechanical properties. However, the …

Multi-component alloys have demonstrated excellent performance in various applications,
but the vast range of possible compositions and microstructures makes it challenging to …

With the coming of the age of artificial intelligence and big data, machine learning (ML) has
been showing powerful potentials for properties prediction of materials. For achieving …