| Abstract Scope |
To accelerate alloy design across a broad compositional space, we investigate two distinct multi-principal element alloy (MPEA) systems by linking atomic-scale structures to mechanical properties. First, we study non-equiatomic AlCoCrFeNi high-entropy alloys (HEAs) to understand how composition-driven variations influence short-range order and yield strength, which are critical for structural performance. Second, we examine transformation-induced plasticity (TRIP) behavior in refractory HEAs (RHEAs) based on TiZrHf with additions of V, Nb, or Ta, aiming to enhance room-temperature ductility while preserving high-temperature strength, thereby mitigating the typical trade-off. Atomistic simulations, including molecular dynamics and hybrid Monte Carlo methods, are employed to extract key structural, mechanical, and thermodynamic descriptors. In parallel, we build a high-throughput database of RHEA compositions, which serves as the foundation for a physics-informed machine learning framework. This integrated approach enables efficient screening and design of MPEAs with tailored mechanical properties, advancing the development of next-generation structural materials through data-driven approaches. |