An informatics guided classification of miscible and immiscible binary alloy systems

Zhang, R. F.; Kong, X. F.; Wang, H. T.; Zhang, S. H.; Legut, D.; Sheng, S. H.; Srinivasan, S.; Rajan, K.; Germann, T. C.

doi:10.1038/s41598-017-09704-1

An informatics guided classification of miscible and immiscible binary alloy systems

Journal Article · Tue Aug 29 04:00:00 EDT 2017 · Scientific Reports

DOI:https://doi.org/10.1038/s41598-017-09704-1· OSTI ID:1624328

Zhang, R. F. ^[1]; Kong, X. F. ^[2]; Wang, H. T. ^[3]; Zhang, S. H. ^[2]; Legut, D. ^[4]; Sheng, S. H. ^[2]; Srinivasan, S. ^[5]; Rajan, K. ^[6]; Germann, T. C. ^[7]

Beihang Univ., Beijing (China). International Research Inst. for Multidisciplinary Science. School of Materials Science and Engineering; DOE/OSTI
Beihang Univ., Beijing (China). International Research Inst. for Multidisciplinary Science. School of Materials Science and Engineering
Chinese Academy of Sciences (CAS), Shenyang (China). CAS Key Lab. of Nuclear Materials and Safety Assessment. Inst. of Metal Research
VSB-Technical Unic. of Ostrava (Czech Republic). IT4Innovations Center. Nanotechnology Centre
Iowa State Univ., Ames, IA (United States). Plant Sciences Inst.
State Univ. of New York (SUNY), Buffalo, NY (United States). Dept. of Materials Design and Innovation
Los Alamos National Lab. (LANL), Los Alamos, NM (United States). Theoretical Div.

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.

View Accepted Manuscript (DOE)

Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division

Grant/Contract Number:: AC52-06NA25396

OSTI ID:: 1624328

Journal Information:: Scientific Reports, Journal Name: Scientific Reports Journal Issue: 1 Vol. 7; ISSN 2045-2322

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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