Abstract
Multi-principal element alloys (MPEAs) have gained extensive interest for structural applications owing to their excellent strength, fracture toughness, wear resistance, creep resistance, and fatigue resistance. In this review, recent progress in the computational design of MPEAs for structural applications is outlined. This includes the scientific advancements achieved through computational methods in the field of structural MPEAs, how new methodologies have emerged due to the needs of complex alloy systems, and adaptations to the existing tools to address emerging problems in the field. We discuss advances in atomistic simulation methods, including structure generation algorithms, element-resolved local lattice distortion, chemical short-range order, local slip resistance, and radiation tolerance, along with experimental comparisons. A detailed discussion on interatomic potentials is included, with a focus on various machine learning-based fitting methods. The application of data science and machine learning for identifying and discovering MPEAs with desirable mechanical performance is summarized and presented.