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High-entropy alloys (HEAs) represent a promising class of materials with exceptional structural and functional properties. However, their design and optimization pose challenges due to the large composition-phase space coupled with the complex and diverse nature of the phase formation dynamics. In this study, a data-driven approach that utilizes machine learning (ML) techniques to predict HEA phases and their composition-dependent phases is proposed. By employing a comprehensive dataset comprising 5692 experimental records encompassing 50 elements and 11 phase categories, we compare the performance of various ML models. Our analysis identifies the most influential features for accurate phase prediction. Furthermore, the class imbalance is addressed by employing data augmentation methods, raising the number of records to 1500 in each category, and ensuring a balanced representation of phase categories. The results show that XGBoost and Random Forest consistently outperform the other models, achieving 86% accuracy in predicting all phases. Additionally, this work provides an extensive analysis of HEA phase formers, showing the contributions of elements and features to the presence of specific phases. We also examine the impact of including different phases on ML model accuracy and feature significance. Notably, the findings underscore the need for ML model selection based on specific applications and desired predictions, as feature importance varies across models and phases. This study significantly advances the understanding of HEA phase formation, enabling targeted alloy design and fostering progress in the field of materials science., (© 2023. Crown.)

The role of grain size and strain rate on the corrosion behavior of plastically-deformed Ti-stabilized austenitic stainless steel (AISI 321) in saline media was investigated. The as-received coarse-grained alloy (CG: ~37 µm) was subjected to thermomechanical processing to develop fine (FG: ~3 µm) and ultrafine (UFG: ~0.24 µm) grained structures. These samples were deformed under high (dynamic) and low (quasi-static) strain-rate conditions to a similar true strain of ~0.86. Microstructural analyses on specimens after deformation prior to corrosion study suggests a shift from the estimated stacking fault energy value of the steel. Electrochemical tests confirm the highest corrosion resistance for UFG specimens due to the formation of the most stable adsorbed passive film. This is followed by FG and CG specimens in that order. For the three grain sizes, the corrosion resistance of specimen deformed under quasi-static loading condition is higher than that subjected to dynamic impact loading while the corrosion resistance of undeformed samples is the least. This work also confirms the non-detrimental effect of TiCs in AISI 321 austenitic stainless steel on its corrosion resistance. However, TiNs were observed to be detrimental by promoting pitting corrosion due to galvanic coupling of TiNs with their surrounding continuous phase. The mechanism of pitting in AISI 321 in chloride solution is proposed.