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- W4383748524 abstract "The difficulty in the prediction of the glass-forming ability (GFA) of alloys hinders the development of new materials with excellent properties. The GFA of alloys is investigated by using three machine learning models based on a decision tree, with a dataset covering 1404 multicomponent alloys collected from peer-reviewed publications. With the composition (including 94 elements, C94), three characteristic temperatures (T3), and three geometric parameters (G3) as the input feature, the XGBoost model achieves the best prediction result {RMSE = 2.5635, R2 = 0.7250}, improved by 11.20% compared with the latest published paper. Among seven possible input feature sets, “C94 + T3” achieves the best with {RMSE = 2.5250, R2 = 0.7332} on the XGBoost model. Further analysis revealed that superimposing G3 on C94 always reduces XGBoost performance; therefore, the GFA of alloys is independent of the atomic size ratio; this is consistent with the fundamental principle of physics: the radii of atoms are determined by element types. A precision of 95.5% is obtained for binary alloys even with an incomplete feature set. Therefore, with “C94 + T3” input features, the XGBoost is generally reliable to effectively predict the GFA of all alloys." @default.
- W4383748524 created "2023-07-11" @default.
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- W4383748524 date "2023-07-10" @default.
- W4383748524 modified "2023-10-16" @default.
- W4383748524 title "Does the GFA of Alloys Depend on the Atomic Size Ratio: A DT-Based ML Study" @default.
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- W4383748524 doi "https://doi.org/10.1021/acs.cgd.3c00509" @default.
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