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Zhang RF, Kong XF, Wang HT, et al. An informatics guided classification of miscible and immiscible binary alloy systems. Sci Rep. 2017;7(1):9577doi: 10.1038/s41598-017-09704-1.
Zhang, R. F., Kong, X. F., Wang, H. T., Zhang, S. H., Legut, D., Sheng, S. H., Srinivasan, S., Rajan, K., & Germann, T. C. (2017). An informatics guided classification of miscible and immiscible binary alloy systems. Scientific reports, 7(1), 9577. https://doi.org/10.1038/s41598-017-09704-1
Zhang, R F, et al. "An informatics guided classification of miscible and immiscible binary alloy systems." Scientific reports vol. 7,1 (2017): 9577. doi: https://doi.org/10.1038/s41598-017-09704-1
Zhang RF, Kong XF, Wang HT, Zhang SH, Legut D, Sheng SH, Srinivasan S, Rajan K, Germann TC. An informatics guided classification of miscible and immiscible binary alloy systems. Sci Rep. 2017 Aug 29;7(1):9577. doi: 10.1038/s41598-017-09704-1. PMID: 28851941; PMCID: PMC5575349.
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Sci Rep. 2017 Aug 29;7(1):9577. doi: 10.1038/s41598-017-09704-1.

An informatics guided classification of miscible and immiscible binary alloy systems.

Scientific reports

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

Affiliations

  1. School of Materials Science and Engineering, and International Research Institute for Multidisciplinary Science, Beihang University, Beijing, 100191, P. R. China. [email protected].
  2. School of Materials Science and Engineering, and International Research Institute for Multidisciplinary Science, Beihang University, Beijing, 100191, P. R. China.
  3. CAS Key Laboratory of Nuclear Materials and Safety Assessment, Institute of Metal Research, Chinese Academy of Sciences, Shenyang, 110016, P.R. China.
  4. IT4Innovations Center & Nanotechnology Centre, VSB-Technical University of Ostrava, CZ-70833, Ostrava, Czech Republic.
  5. Plant Sciences Institute, Iowa State University, 2031 Roy J. Carver Co-Lab, Ames, IA, 50011, USA.
  6. Department of Materials Design and Innovation, University at Buffalo-State University of New York, 311 Bell Hall, Buffalo, NY, 14260, USA.
  7. Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.

PMID: 28851941 PMCID: PMC5575349 DOI: 10.1038/s41598-017-09704-1

Abstract

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.

References

  1. Nat Mater. 2013 Mar;12(3):191-201 - PubMed
  2. Sci Rep. 2013 Sep 30;3:2810 - PubMed

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