摘要：The classification of miscible and immiscible systems of binary alloys plays a critical role in the design of multicomponent alloys. By mining data from hundreds of experimental phase diagrams, and thousands of thermodynamic data sets from experiments and high-throughput first-principles (HTFP) calculations, we have obtained a comprehensive classification of alloying behavior for 813 binary alloy systems consisting of transition and lanthanide metals. Among several physics-based descriptors, the slightly modified Pettifor chemical scale provides a unique two-dimensional map that divides the miscible and immiscible systems into distinctly clustered regions. Based on an artificial neural network algorithm and elemental similarity, the miscibility of the unknown systems is further predicted and a complete miscibility map is thus obtained. Impressively, the classification by the miscibility map yields a robust validation on the capability of the well-known Miedema's theory (95% agreement) and shows good agreement with the HTFP method (90% agreement). Our results demonstrate that a state-of-the-art physics-guided data mining can provide an efficient pathway for knowledge discovery in the next generation of materials design.
