The accelerating global demand for critical minerals, driven by clean energy technologies and climate goals, presents urgent sustainability challenges in materials design. High-entropy alloys (HEAs), particularly FeNiCrCoAl, offer a promising alternative by enabling reduced reliance on critical elements such as Ni, Cr, and Co. This study introduces a data-driven framework that integrates molecular dynamics (MD) simulations with artificial intelligence (AI), specifically machine learning (ML), to predict and optimize the mechanical performance of FeNiCrCoAl HEAs. MD simulations generated over 1800 datasets capturing ultimate tensile strength (UTS) across diverse compositions and temperatures. These data were used to train the Random Forest ML models, achieving high predictive accuracy (R2 = 0.975, RMSE = 0.22). Explainable AI techniques revealed Ni as a key contributor to strength, enabling targeted reduction of Co, Cr, and Al. A novel composition was discovered that reduced critical element content by over 50% achieving nearly double the UTS while retaining more than 90% of its tensile strength across the temperature range. This integrated MD-ML approach provides a scalable and sustainable pathway for alloy design, bridging atomic-scale simulation with predictive modeling to address global resource efficiency goals.
Achmad et al. (Mon,) studied this question.