Design of High-Entropy Alloys Using Conditional Generative Models and Multi-objective Bayesian Optimization

Key Points

• The aim is to improve the design process of high-entropy alloys by employing advanced computational techniques.
• Utilized conditional generative models to predict material properties.
• Implemented multi-objective bayesian optimization for alloy design.
• Investigated compositions ranging from 5 to 35 atomic percent.
• Successfully identified optimal compositions for high-entropy alloys.
• Showed enhanced predictive accuracy for material properties compared to traditional methods.