The field of multi-principal element or (single-phase) high-entropy (HE) alloys has recently seen exponential growth as these systems represent a paradigm shift in alloy development, in some cases exhibiting unexpected structures and superior mechanical properties. However, the identification of promising HE alloys presents a daunting challenge given the associated vastness of the chemistry/composition space. We describe here a supervised learning strategy for the efficient screening of HE alloys that combines two complementary tools, namely: (1) a multiple regression analysis and its generalization, a canonical-correlation analysis (CCA) and (2) a genetic algorithm (GA) with a CCA-inspired fitness function. These tools permit the identification of promising multi-principal element alloys. We implement this procedure using a database for which mechanical property information exists and highlight new alloys having high hardnesses. Our methodology is validated by comparing predicted hardnesses with alloys fabricated by arc-melting, identifying alloys having very high measured hardnesses.
An electron diffraction and microscopy study is presented of a variety of phases in the Y:Ba:Cu:O system in which superconductivity occurs. The superconducting phase is demonstrated by convergent beam electron diffraction to be centrosymmetric with space group Pmmm, in contrast to a previous determination of Pmm2. This discrepancy arises from local symmetry-breaking defects. In addition to this phase and a cubic BaCuO2 phase, we characterize two other phases. One is the Y-rich orthorhombic phase: Pnma with a=13.5 Å, b=6.3 Å, and c=7.6 Å. The second occurs by a phase transition of the superconducting Pmmm phase to P4/mmm with a=3.85 Å, c=11.7 Å. The superconducting phase may now be described as either an ordered array of oxygen vacancies in the perovskite structure, or an ordered array of oxygen interstitials in the new tetragonal phase, which may explain how the material can lose oxygen reversibly.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.