Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/94236
Title: | Machine learning prediction of glass-forming ability in bulk metallic glasses | Authors: | Xiong, J Shi, SQ Zhang, TY |
Issue Date: | May-2021 | Source: | Computational materials science, May 2021, v. 192, 110362 | Abstract: | The critical casting diameter (Dmax) quantitatively represents glass-forming ability (GFA) of bulk metallic glasses (BMGs). The present work constructed a dataset of two subsets, L-GFA subset of 376 BMGs with 1 mm ≤Dmax < 5 mm and G-GFA subset of 319 BMGs with Dmax ≥ 5 mm. The sequential backward selector and exhaustive feature selector are introduced to select key features. The trained XGBoost classifier with four selected features is able to successfully classify the L-GFA and G-GFA BMGs. Furthermore, the trained XGBoost regression model with another four selected features predicts the Dmax of G-GFA samples with a cross-validated correlation coefficient of 0.8012. The correlation between features and Dmax will provide the guidance in the design and discovery of novel BMGs. | Keywords: | Bulk metallic glasses Glass-forming ability Machine learning XGBoost |
Publisher: | Elsevier | Journal: | Computational materials science | ISSN: | 0927-0256 | DOI: | 10.1016/j.commatsci.2021.110362 | Rights: | © 2021 Elsevier B.V. All rights reserved. © 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/. The following publication Xiong, J., et al. (2021). "Machine learning prediction of glass-forming ability in bulk metallic glasses." Computational Materials Science 192: 110362 is available at https://dx.doi.org/10.1016/j.commatsci.2021.110362. |
Appears in Collections: | Journal/Magazine Article |
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Xiong_Machine_Learning_Prediction.pdf | Pre-Published version | 1.65 MB | Adobe PDF | View/Open |
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