Synergistic prediction of phase and hardness in high-entropy alloys via the integration of machine learning and active learning strategies

Key Points

• This research aims to enhance the predictive accuracy of microstructure and properties in high-entropy alloys using advanced machine learning methods.
• Integration of machine learning and active learning strategies
• Focus on the extensive compositional space of high-entropy alloys
• Utilization of limited sample sizes to improve model predictions
• Improved prediction accuracy of microstructure and properties in high-entropy alloys
• Demonstrated effectiveness of combining machine learning with active learning strategies
• Highlighted challenges associated with extensive compositional space and limited sample sizes