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Supervised machine learning-based multi-class phase prediction in high-entropy alloys using robust databases
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DOI：10.1016/j.jallcom.2023.171224.png)
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This work evaluated the phase prediction capability of high entropy alloys using four supervised machine learning models K-Nearest Neighbors (KNN), Multinomial Regression, Extreme Gradient Boosting (XGBoost), and Random Forest. The study addresses the challenge of predicting multicomponent alloys by considering the overlapping of multicategorical stability parameters. Eight prediction classes (FCC, BCC, FCC+BCC, FCC+Im, BCC+Im, FCC+BCC+Im, Im and AM) were used. Finally, the predicted results were compared with those of two new alloys fabricated by induction melting in a controlled atmosphere using X-ray diffraction (XRD). The results indicate that with a robust database, appropriate data treatment, and training, satisfactory and competitive prediction indicators can be obtained with traditional machine learning predictions based on four prediction classes: Solid Solution (SS), Solid Solution with Intermetallic (SS+Im), intermetallic (Im), and amorphous (AM). The best predictive model obtained from the four evaluated models was Random Forest, with an accuracy of 72.8% and ROC AUC of 93.1%.
Keyword:
Phase prediction
High entropy alloys
Machine Learning
Intermetallics prediction
期刊
IF:
6.3
论文数:
8.4W
被引数:
24.3W
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引用论文
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