High-entropy alloys (HEAs) exhibit complex phase formation behavior, challenging conventional predictive methods. This study presents a machine learning (ML) framework for phase prediction in HEAs, using a curated dataset of 648 experimentally characterized compositions and features derived from thermodynamic and electronic descriptors. Three classifiers—random forest, gradient boosting, and CatBoost—were trained and validated through cross-validation and testing. Gradient boosting achieved the highest accuracy, and valence electron concentration (VEC), atomic size mismatch (δ), and enthalpy of mixing (ΔHmix) were identified as the most influential features. The model predictions were experimentally verified using a non-equiatomic Al30Cu17.5Fe17.5Cr17.5Mn17.5 alloy and the equiatomic Cantor alloy (CoCrFeMnNi), both of which showed strong agreement with predicted phase structures. The results demonstrate that combining physically informed feature engineering with ML enables accurate and generalizable phase prediction, supporting accelerated HEA design.

1. Introduction
2. Methodology
    2.1. Dataset Compilation and Preprocessing
    2.2. Feature Engineering and Descriptor Calculation
    2.3. Machine Learning Model Development
    2.4. Model Training, Validation, and Feature Importance Analysis
    2.5. Experimental Synthesis and Validation
3. Results and Discussion
    3.1. Exploratory Data Analysis and Feature Relevance
    3.2. Machine Learning Model Performance and Feature Importance
    3.3. Model Application and Experimental Validation
4. Conclusion
Funding
Conflict of interest
Data Availability Statement
Author Information and Contribution
Acknowledgments
References

• Nguyen Lam Khoa(Faculty of Materials Engineering, School of Materials Science and Engineering, Hanoi University of Science and Technology, Hanoi 100000, Vietnam)
• Nguyen Duy Khanh(Faculty of Materials Engineering, School of Materials Science and Engineering, Hanoi University of Science and Technology, Hanoi 100000, Vietnam)
• Hoang Thi Ngoc Quyen(Faculty of Materials Engineering, School of Materials Science and Engineering, Hanoi University of Science and Technology, Hanoi 100000, Vietnam)
• Nguyen Thi Hoang Oanh(Faculty of Materials Engineering, School of Materials Science and Engineering, Hanoi University of Science and Technology, Hanoi 100000, Vietnam)
• Le Hong Thang(Faculty of Materials Engineering, School of Materials Science and Engineering, Hanoi University of Science and Technology, Hanoi 100000, Vietnam)
• Nguyen Hoa Khiem(Faculty of Materials Engineering, School of Materials Science and Engineering, Hanoi University of Science and Technology, Hanoi 100000, Vietnam)
• Nguyen Hoang Viet(Faculty of Materials Engineering, School of Materials Science and Engineering, Hanoi University of Science and Technology, Hanoi 100000, Vietnam) Corresponding author