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.

CAS Key Laboratory of Nuclear Materials and Safety Assessment Institute of Metal Research Chinese Academy of Sciences Shenyang 110016 P R China

Department of Materials Design and Innovation University at Buffalo State University of New York 311 Bell Hall Buffalo NY 14260 USA

IT4Innovations Center and Nanotechnology Centre VSB Technical University of Ostrava CZ 70833 Ostrava Czech Republic

Plant Sciences Institute Iowa State University 2031 Roy J Carver Co Lab Ames IA 50011 USA

School of Materials Science and Engineering and International Research Institute for Multidisciplinary Science Beihang University Beijing 100191 P R China

Theoretical Division Los Alamos National Laboratory Los Alamos NM 87545 USA

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