Modeling High-Entropy Alloys: Challenges, Progress, and Future Directions

High-entropy alloys (HEAs) offer a vast compositional landscape that challenges traditional alloy design, requiring robust computational frameworks. This review critically examines the current state of computational modeling of HEAs, focusing on phase stability, mechanical behavior, and data-driven design. We assess the strengths and limitations of CALPHAD (calculation of phase diagrams), first-principles-based methods, effective medium approaches, and emerging machine-learning-enabled frameworks, highlighting how their integration can overcome scalability and accuracy barriers. Additionally, we discuss emerging data-driven strategies, such as Bayesian optimization and active learning, which are shifting the paradigm from predictive modeling to inverse design. We conclude by outlining key challenges and future directions, emphasizing the need for interpretable, uncertainty-quantified, and sustainability-aware modeling frameworks to enable predictive and responsible design of next-generation HEAs.