, Volume 68, Issue 8, pp 2116–2125 | Cite as

Semi-Supervised Approach to Phase Identification from Combinatorial Sample Diffraction Patterns

  • Jonathan Kenneth Bunn
  • Jianjun Hu
  • Jason R. Hattrick-Simpers


Manual attribution of crystallographic phases from high-throughput x-ray diffraction studies is an arduous task, and represents a rate-limiting step in high-throughput exploration of new materials. Here, we demonstrate a semi-supervised machine learning technique, SS-AutoPhase, which uses a two-step approach to identify automatically phases from diffraction data. First, clustering analysis is used to select a representative subset of samples automatically for human analysis. Second, an AdaBoost classifier uses the labeled samples to identify the presence of the different phases in diffraction data. SS-AutoPhase was used to identify the metallographic phases in 278 diffraction patterns from a FeGaPd composition spread sample. The accuracy of SS-AutoPhase was >82.6% for all phases when 15% of the diffraction patterns were used for training. The SS-AutoPhase predicted phase diagram showed excellent agreement with human expert analysis. Furthermore it was able to determine and identify correctly a previously unreported phase.


Hierarchal Cluster Analysis Human Expert Dynamic Time Warping Fe3Si Training Sample Size 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The work is funded in part by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy, under Award DE-AR0000492. We would like to acknowledge the support of the South Carolina SmartState Center for Strategic Approaches to the Generation of Electricity (SAGE).

Supplementary material

11837_2016_2033_MOESM1_ESM.docx (397 kb)
Supplementary material 1 (DOCX 397 kb)


  1. 1.
    National Science and Technology Council, Materials Genome Initiative for Global Competitiveness (2011).Google Scholar
  2. 2.
    S. Curtarolo, G.L.W. Hart, M.B. Nardelli, N. Mingo, S. Sanvito, and O. Levy, Nat. Mater. 12, 191 (2013).CrossRefGoogle Scholar
  3. 3.
    National Science and Technology Council, Materials Genome Initiative Strategic Plan (2014).Google Scholar
  4. 4.
    M.L. Green, J.R. Hattrick-Simpers, I. Takeuchi, S.C. Barron, A.M. Joshi, T. Chiang, A. Mehta, and A. Davydov, Fulfilling the Promise of the Materials Genome Initiative via High-Throughput Experimentation (2014).Google Scholar
  5. 5.
    J.R. Hattrick-Simpers, C. Wen, and J. Lauterbach, Catal. Lett. 145, 290 (2014).CrossRefGoogle Scholar
  6. 6.
    D.J. Arriola, E.M. Carnahan, P.D. Hustad, R.L. Kuhlman, and T.T. Wenzel, Science 714, 312 (2006).Google Scholar
  7. 7.
    J. Cui, Y.S. Chu, O.O. Famodu, Y. Furuya, J.R. Hattrick-Simpers, R.D. James, A. Ludwig, S. Thienhaus, M. Wuttig, Z. Zhang, and I. Takeuchi, Nat. Mater. 4, 286 (2006).CrossRefGoogle Scholar
  8. 8.
    A. Shinde, D. Guevarra, J.A. Haber, J. Jin, and J.M. Gregoire, J. Mater. Res. 30, 442 (2015).CrossRefGoogle Scholar
  9. 9.
    W.F. Maier, K. Stowe, and S. Sieg, Angew. Chem. Int. Ed. Engl. 46, 6016 (2007).CrossRefGoogle Scholar
  10. 10.
    O.O. Famodu, J.R. Hattrick-Simpers, M. Aronova, K. Chang, M. Murakami, M. Wuttig, T. Okazaki, Y. Furuya, L.A. Knauss, L.A. Bendersky, F.S. Biancaniello, and I. Takeuchi, Mater. Trans. 45, 173 (2004).CrossRefGoogle Scholar
  11. 11.
    A. Holzwarth and W.F. Maier, Platin. Met. Rev. 44, 16 (2000).Google Scholar
  12. 12.
    K. Yang, J. Bedenbaugh, H. Li, M. Peralta, J.K. Bunn, J. Lauterbach, and J.R. Hattrick-Simpers, ACS Comb. Sci. 14, 372 (2012).CrossRefGoogle Scholar
  13. 13.
    G. Barr, W. Dong, and C.J. Gilmore, J. Appl. Crystallogr. 37, 243 (2004).CrossRefGoogle Scholar
  14. 14.
    G.J. Cunningham (Master’s Thesis, Instituto Superior Técnico, 2011).Google Scholar
  15. 15.
    C.J. Long, J.R. Hattrick-Simpers, M. Murakami, R.C. Srivastava, I. Takeuchi, V.L. Karen, and X. Li, Rev. Sci. Instrum. 78, 072217 (2007).CrossRefGoogle Scholar
  16. 16.
    R. Le Bras, T. Damoulas, J.M. Gregoire, A. Sabharwal, C.P. Gomes, and R.B. Van Dover, Lect. Notes Comput. Sci. 6878, 508 (2011).Google Scholar
  17. 17.
    S. Ermon, R. Le Bras, S.K. Suram, J.M. Gregoire, C.P. Gomes, B. Selman, and R.B. Van Dover, arXiv. 1411, 7441 (2014).Google Scholar
  18. 18.
    L.A. Baumes, M. Moliner, N. Nicoloyannis, and A. Corma, Cryst. Eng. Comm. 10, 10 (2008).CrossRefGoogle Scholar
  19. 19.
    C.J. Long, D. Bunker, X. Li, V.L. Karen, and I. Takeuchi, Rev. Sci. Instrum. 80, 1 (2009).CrossRefGoogle Scholar
  20. 20.
    A.G. Kusne, T. Gao, A. Mehta, L. Ke, M.C. Nguyen, K.M. Ho, V. Antropov, C.Z. Wang, M.J. Kramer, C. Long, and I. Takeuchi, Sci. Rep. 4, 6367 (2014).CrossRefGoogle Scholar
  21. 21.
    J.K. Bunn, S. Han, Y. Tong, Y. Zhang, J. Hu, and J.R. Hattrick-Simpers, J. Mater. Res. 30, 879 (2015).CrossRefGoogle Scholar
  22. 22.
    Citrin Informatics, Fe-Ga-Pd, Ciritrination,
  23. 23.
  24. 24.
    F. Pedregosa and G. Varoquaux, J. Mach. Learn. 12, 2825 (2011).MathSciNetGoogle Scholar
  25. 25.
    J.A. Hartigan and M.A. Wong, J. R. Stat. Soc. C App. 28, 100 (1979).Google Scholar
  26. 26.
    D. Arthur and S. Vassilvitskii, Proceedings of Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, p. 1027 (2007).Google Scholar
  27. 27.
    Y. Freund and R.E. Schapire, J. Comput. Syst. Sci. 55, 119 (1997).MathSciNetCrossRefGoogle Scholar
  28. 28.
    K. Rajan, C. Suh, and P.F. Mendez, Stat. Anal. Data Min. 1, 361 (2009).MathSciNetCrossRefGoogle Scholar
  29. 29.
    J.R. Hattrick-Simpers, J. Cui, M. Murakami, A. Orozco, L. Knauss, R.J. Booth, E.W. Greve, S.E. Lofland, M. Wuttig, and I. Takeuchi, Appl. Surf. Sci. 254, 734 (2007).CrossRefGoogle Scholar
  30. 30.
    J. Cui, T.W. Shield, and R.D. James, Acta Mater. 52, 35 (2004).CrossRefGoogle Scholar
  31. 31.
    J. Cui (PhD Thesis, University of Minnesota 2002).Google Scholar
  32. 32.
    R.A. Kellogg, A.B. Flatau, A.E. Clark, M. Wun-Fogle, and T.A. Lograsso, J. Appl. Phys. 93, 8495 (2003).CrossRefGoogle Scholar
  33. 33.
    M. Wuttig, L. Dai, and J. Cullen, Appl. Phys. Lett. 80, 113501137 (2002).CrossRefGoogle Scholar
  34. 34.
    S. Hamann, M.E. Gruner, S. Irsen, J. Buschbeck, C. Bechtold, I. Kock, S.G. Mayr, A. Savan, S. Thienhaus, E. Quandt, E.S. Fohler, P. Entel, and A. Ludwig, Acta Mater. 58, 5949 (2010).CrossRefGoogle Scholar
  35. 35.
    S. Curtarolo, W. Setyawan, S. Wang, J. Xue, K. Yang, R.H. Taylor, L.J. Nelson, G.L.W. Hart, S. Sanvito, M. Buongiorno-Nardelli, N. Mingo, and O. Levy, Comp. Mater. Sci. 58, 227 (2012).CrossRefGoogle Scholar
  36. 36.
    A. Jain, G. Hautier, C.J. Moore, S.P. Ong, C.C. Fischer, T. Mueller, K.A. Persson, and G. Ceder, Mater. Sci. 50, 2295 (2011).Google Scholar
  37. 37.
    D. Landis, J.S. Hummelshoj, S. Nestorov, J. Greeley, M. Dulak, T. Bligaard, J.K. Norskov, and K. Jaconsen, Comput. Sci. Eng. 14, 51 (2012).CrossRefGoogle Scholar
  38. 38.
    M. Klintenberg, The Electronic Structure Project,
  39. 39.
    E. Tadmor, R. Elliot, and I. Takeuichi, Rise of Data in Materials Research,
  40. 40.
    J.R. Hattrick-Simpers, J.M. Gregoire, and A.G. Kusne, APL Mater. 4, 053211 (2016).CrossRefGoogle Scholar

Copyright information

© The Minerals, Metals & Materials Society 2016

Authors and Affiliations

  1. 1.SmartState Center for the Strategic Approaches to the Generation of Electricity, Department of Chemical EngineeringUniversity of South CarolinaColumbiaUSA
  2. 2.Department of Computer Science and EngineeringUniversity of South CarolinaColumbiaUSA

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