Probabilistic Assessment of Glass Forming Ability Rules for Metallic Glasses Aided by Automated Analysis of Phase Diagrams
Author:
Funder
National Science Foundation
Erich Bloch Endowment
Publisher
Springer Science and Business Media LLC
Subject
Multidisciplinary
Link
http://www.nature.com/articles/s41598-018-36224-3.pdf
Reference49 articles.
1. Meredig, B. et al. Combinatorial screening for new materials in unconstrained composition space with machine learning. Physical Review B 89, 094104 (2014).
2. Ri, J. B., Wen, Z. & Jiang, Q. A criterion for the glass-forming ability of binary bulk metallic glasses. Journal of Non-crystalline Solids 471, 264–267, https://doi.org/10.1016/j.jnoncrysol.2017.06.004 (2017).
3. Sun, Y. T., Bai, H. Y., Li, M. Z. & Wang, W. H. Machine Learning Approach for Prediction and Understanding of Glass-Forming Ability. Journal of Physical Chemistry Letters 8, 3434–3439, https://doi.org/10.1021/acs.jpclett.7b01046 (2017).
4. Turnbull, D. Under what conditions can a glass be formed? Contemporary Physics 10, 473–488, https://doi.org/10.1080/00107516908204405 (1969).
5. Johnson, W. L. Bulk Glass-Forming Metallic Alloys: Science and Technology. MRS Bulletin 24, 42–56, https://doi.org/10.1557/S0883769400053252 (2013).
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