Ye Wei

TL;DR: Hu et al. as mentioned in this paper proposed an active learning strategy to accelerate the design of high-entropy Invar alloys in a practically infinite compositional space based on very sparse data, which works as a closed-loop, integrating machine learning with density functional theory, thermodynamic calculations, and experiments.

TL;DR: This crystal-glass high-entropy nanocomposite design concept provides a new approach to developing advanced materials with an outstanding combination of strength and ductility.

TL;DR: In this paper, a novel concept to overcome this dilemma is proposed, by developing bulk SMMs with excellent mechanical and attractive soft magnetic properties through coherent and ordered nanoprecipitates ( 42%) of nanoprecipes achieves significantly enhanced strength (1526 MPa) at good ductility (15%), while the coercivity is only marginally increased (<10.7 Oe).

TL;DR: In this article , a FeCoNiTa-Al multicomponent alloy (MCA) with paramagnetic coherent nanoparticles was designed to impede dislocation motion, enhancing strength and ductility.

TL;DR: A scientific application explored in this study is to combine the boosting tracker and the Hough transformation, followed by principal component analysis, to extract the location and trace of grain boundaries within atom probe data.