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 …

Materials development has historically been driven by human needs and desires, and this is
likely to continue in the foreseeable future. The global population is expected to reach ten …

Abstract Machine learning (ML) models provide great opportunities to accelerate novel
material development, offering a virtual alternative to laborious and resource-intensive …

In this article we provide an overview on the current and emerging applications of machine
learning (ML) in the design, synthesis, and characterization of metal matrix composites …

Superhydrophobic surfaces have been largely achieved through various surface
topographies. Both empirical and numerical simulations have been reported to help …

Abstract A series of Y 2 O 3, Y 2 O 3: Er 3+(1%) and Y 2 O 3-AG: Er 3+(1%)(where AG= BO 3
3−, PO 4 3−, and SO 4 2−) nanophosphors were prepared via a chemical combustion …

Artificial intelligence-based materials application is one of the hot topics in the field of
materials science. Materials are widely used in the space industry, cutting tools, thermal and …

Owing the rapid development of big data, artificial intelligence, and highperformance
computing, the research and development of data-driven materials has intensified. During …

This paper presents an evolutionary based method to obtain the un-stressed lattice spacing,
d 0, required to calculate the residual stress profile across a weld of an age-hardenable …

Residual stresses are originated during manufacturing processes of metallic materials, so its
study is important to avoid catastrophic accidents during component service. There are two …