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Prediction of cooling rate dependent ordering in metallic glass transition using a two-state model AITranslate

Iowa State University; Iowa State University; Renmin University of China; Iowa State University; Iowa State University; Iowa State University
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Publisher: Elsevier
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Abstract AITranslate

A two-state theoretical model is proposed to study the evolution of local order when a good metallic glass former is cooled down from the liquid state. We find that the development of order depends strongly on the cooling rate and that the ordered fraction converges to an upper limit at low cooling rates. We compare our model predictions with molecular dynamics (MD) simulation results for the Zr35.5Cu64.5 binary system, revealing good agreement for the fast cooling rates accessible through MD. The analytical model proposed here, however, can be extended to much lower rates which correspond to experimentally accessible processing routes.

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DOI:https://doi.org/10.1016/j.commatsci.2010.06.002

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Citation Information:

A two-state theoretical model is proposed to study the evolution of local order when a good metallic glass former is cooled down from the liquid state. We find that the development of order depends strongly on the cooling rate and that the ordered fraction converges to an upper limit at low cooling rates. We compare our model predictions with molecular dynamics (MD) simulation results for the Zr35.5Cu64.5 binary system, revealing good agreement for the fast cooling rates accessible through MD. The analytical model proposed here, however, can be extended to much lower rates which correspond to experimentally accessible processing routes.

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GB/T 7714-2015 [1] S.G. Hao, C.Z. Wang, Maozhi Li, et al. Computational Materials Science, 2010(49). DOI:10.1016/j.commatsci.2010.06.002.
MLA [1] S.G. Hao, et al., Computational Materials Science, no. 49, 2010, https://doi.org/10.1016/j.commatsci.2010.06.002.
APA [1] S.G. Hao, C.Z. Wang, Maozhi Li, R.E. Napolitano, M.I. Mendelev, & K.M. Ho. (2010). Computational Materials Science(49). https://doi.org/10.1016/j.commatsci.2010.06.002
IEEE [1] S.G. Hao, C.Z. Wang, Maozhi Li, R.E. Napolitano, M.I. Mendelev, and K.M. Ho, Computational Materials Science, no. 49, 2010, doi: 10.1016/j.commatsci.2010.06.002.