Details

Autor(en) / Beteiligte

• Oñate, Angelo
• Sanhueza, Juan Pablo
• Zegpi, Diabb
• Tuninetti, Víctor
• Ramirez, Jesús
• Medina, Carlos
• Melendrez, Manuel
• Rojas, David

Titel

Supervised machine learning-based multi-class phase prediction in high-entropy alloys using robust databases

Ist Teil von

• Journal of alloys and compounds, 2023-11, Vol.962, p.171224, Article 171224

Ort / Verlag

Elsevier B.V

Erscheinungsjahr

2023

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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%. •Machine learning was applied to 2434 experimental data for phase prediction.•The best evaluated model is Random Forest with 72.8% Accuracy and 93.1% ROC AUC.•An adequate prediction was found for two new HEAs obtained experimentally.•The packing factor may be the key to the increase in HEA prediction performance.