The history of machine learning (ML) can be traced back to the 1950 s, and its application in
alloy design has recently begun to flourish and expand rapidly. The driving force behind this …

Defects determine many important properties and applications of materials, ranging from
doping in semiconductors, to conductivity in mixed ionic–electronic conductors used in …

With the development of artificial intelligence, machine learning has a wide range of
applications in the field of materials. The sparsity of data on the mechanical properties of …

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 …

Despite a plethora of data being generated on the mechanical behavior of multi-principal
element alloys, a systematic assessment remains inaccessible via Edisonian approaches …

Abstract Characterization of material structure with X-ray or neutron scattering using eg Pair
Distribution Function (PDF) analysis most often rely on refining a structure model against an …

Two data-driven machine learning (ML) models were proposed for the hardness prediction
of high-entropy alloys (HEA) and the composition optimization of high hardness HEAs …

Typically, magnesium alloys have been designed using a so-called hill-climbing approach,
with rather incremental advances over the past century. Iterative and incremental alloy …

Lattice distortion is a basic characteristic of multi-principal element alloys (MPEAs), or high
entropy alloys (HEAs). The severe lattice distortion strategy is an effective way to improve …

High entropy alloys (HEAs), especially refractory HEAs, have become a subject of interest in
the past years due to their exceptional properties in terms of high-temperature strength …