This review discussed the dilemma of small data faced by materials machine learning. First,
we analyzed the limitations brought by small data. Then, the workflow of materials machine …

This comprehensive review investigates the multifaceted applications of machine learning in
materials research across six key dimensions, redefining the field's boundaries. It explains …

As the big data generated by the development of modern experiments and computing
technology becomes more and more accessible, the material design method based on …

High-entropy alloys (HEAs) have garnered significant attention across various fields owing
to their unique design incorporating multi-principal elements and remarkable …

The review summarizes some achievements of materials scientists in designing high
entropy alloys (HEAs) and developing production routs for their industrial implementation, as …

The development of the aviation industry is accompanied by the continuous research of high-
performance aviation aluminum alloys. Stuck in vast untapped composition space and the …

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 …

Compared to conventional alloys, multicomponent high-entropy alloys (HEAs) have
received considerable attention in recent years owing to their exceptional phase stability …

In this research, for some different Schottky type structures with and without a
nanocomposite interfacial layer, the current–voltage (I–V) characteristics have been …

Accurate and efficient prediction of the stability and structure–stability relationship is
important to discover materials; however, it requires tremendous efforts via traditional trial …