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There is a genuine need to shorten the development period for new materials with desired properties. In this work, machine learning (ML) was conducted on a dataset of the elastic moduli of 219 bulk-metallic glasses (BMGs) and another dataset of the critical casting diameters (Dmax) of 442 BMGs. The resulting ML model predicted the moduli and Dmax of BMGs in good agreement with most experimentally measured values, and the model even identified some errors reported in the literature. This work indicates the great potential of ML in design of advanced materials with target properties.
Cu0.62Zn0.38 foil was subjected to surface mechanical attrition treatment (SMAT) processing first. Growth behavior of ZnO nanostructure on the SMAT Cu0.62Zn0.38 surface during thermal oxidation was investigated in this paper. The original and SMAT Cu0.62Zn0.38 foils were thermally oxidized at 400 ~ 700 °C under various gaseous environments, including nitrogen and mixture of N2-O2 at a pressure of 1 atm. for 3 h. The oxidized specimens were characterized with a scanning electron microscope, an X-ray diffractometer and a transmission electron microscope. It is found that nanosheets are easily formed on the SMAT specimen surface. The favorable formation of nanosheets relates to twin lamellae structure of Cu0.62Zn0.38 formed during SMAT processing.
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