| Abstract Scope |
Multi-Principal Element Alloys (MPEAs) have gained widespread attention due to their exceptional mechanical properties, including high strength, ductility, wear resistance, and thermal stability. These characteristics arise from their complex chemical compositions and microstructural features, such as severe lattice distortion and chemical short-range order. Predictive computational modeling plays a pivotal role in uncovering the mechanisms that govern mechanical behavior and in guiding the design of MPEAs. This talk offers a critical perspective on multiscale computational techniques—including density functional theory, molecular dynamics, CALPHAD, phase-field methods, and machine learning—for understanding how processing routes and evolving microstructures influence mechanical response. Special emphasis is placed on capturing process–structure–mechanical property relationships, including phase transformations, dislocation behavior, strain localization, and deformation mechanisms under various loading conditions. Key challenges and opportunities in bridging time and length scales are discussed. The talk concludes with recommendations for integrating physics-based and data-driven models to accelerate the design of mechanically robust MPEAs. |