Advertisement

JOM

, Volume 69, Issue 5, pp 886–892 | Cite as

High-Throughput Thermodynamic Modeling and Uncertainty Quantification for ICME

  • Richard A. Otis
  • Zi-Kui Liu
Article

Abstract

One foundational component of the integrated computational materials engineering (ICME) and Materials Genome Initiative is the computational thermodynamics based on the calculation of phase diagrams (CALPHAD) method. The CALPHAD method pioneered by Kaufman has enabled the development of thermodynamic, atomic mobility, and molar volume databases of individual phases in the full space of temperature, composition, and sometimes pressure for technologically important multicomponent engineering materials, along with sophisticated computational tools for using the databases. In this article, our recent efforts will be presented in terms of developing new computational tools for high-throughput modeling and uncertainty quantification based on high-throughput, first-principles calculations and the CALPHAD method along with their potential propagations to downstream ICME modeling and simulations.

Keywords

Robust Optimization Uncertainty Quantification Integrate Computational Material Engineering CALPHAD Method Phase Equilibrium Data 
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.

Notes

Acknowledgements

This work was supported by a NASA Space Technology Research Fellowship under Grant NNX14AL43H.

References

  1. 1.
    L. Kaufman, Prog. Mater Sci. 14, 55 (1969).CrossRefGoogle Scholar
  2. 2.
    L. Kaufman and H. Bernstein, Computer Calculation of Phase Diagram (Waltham: Academic Press Inc., 1970).Google Scholar
  3. 3.
    P.J. Spencer, CALPHAD 32, 1 (2008).CrossRefGoogle Scholar
  4. 4.
    N. Saunders and A.P.P. Miodownik, CALPHAD (Calculation of Phase Diagrams): A Comprehensive Guide (Oxford: Pergamon, 1998).Google Scholar
  5. 5.
    H.L. Lukas, S.G. Fries, and B. Sundman, Computational Thermodynamics: The CALPHAD Method (Cambridge: Cambridge University Press, 2007).CrossRefzbMATHGoogle Scholar
  6. 6.
    Z.K. Liu and Y. Wang, Computational Thermodynamics of Materials (Cambridge: Cambridge University Press, 2016).CrossRefGoogle Scholar
  7. 7.
    National Research Council, Integrated Computational Materials Engineering: A Transformational Discipline for Improved Competitiveness and National Security (2008).Google Scholar
  8. 8.
    Z.K. Liu, J. Phase Equilib. Diff. 30, 517 (2009).CrossRefGoogle Scholar
  9. 9.
    Z.K. Liu and D.L. McDowell, Integr. Mater. Manuf. Innov. 3, 28 (2014).CrossRefGoogle Scholar
  10. 10.
    National Science and Technology Council. Materials Genome Initiative for Global Competitiveness (2011). https://www.mgi.gov/sites/default/files/documents/materials_genome_initiative-final.pdf. Accessed 22 March 2017.
  11. 11.
    G.B. Olson, Scr. Mater. 70, 1 (2014).CrossRefGoogle Scholar
  12. 12.
    L. Kaufman and J. Agren, Scr. Mater. 70, 3 (2014).CrossRefGoogle Scholar
  13. 13.
    Z.K. Liu, Chin. Sci. Bull. 59, 1619 (2014).CrossRefGoogle Scholar
  14. 14.
    A.T. Dinsdale, CALPHAD 15, 317 (1991).CrossRefGoogle Scholar
  15. 15.
    C.E. Campbell, U.R. Kattner, and Z.K. Liu, Integr. Mater. Manuf. Innov. 3, 12 (2014).CrossRefGoogle Scholar
  16. 16.
    Y. Wang, S. Curtarolo, C. Jiang, R. Arroyave, T. Wang, G. Ceder, L.Q. Chen, and Z.K. Liu, CALPHAD 28, 79 (2004).CrossRefGoogle Scholar
  17. 17.
    S. Curtarolo, D. Morgan, and G. Ceder, CALPHAD 29, 163 (2005).CrossRefGoogle Scholar
  18. 18.
    S. Shang, Y. Wang, and Z.K. Liu, Magnesium Technology 2010, eds. S.R. Agnew, N.R. Neelameggham, E.A. Nyberg, and W.H. Sillekens (2010), pp. 617–622.Google Scholar
  19. 19.
    Y.Q. Sun (PhD Thesis, The Pennsylvania State University, 2016).Google Scholar
  20. 20.
    Y.Q. Sun, Z.K. Liu, T. Yao, and Q. Du (2017, unpublished).Google Scholar
  21. 21.
    J. Allison, B. Liu, K. Boyle, R. Beals, and L. Hector, Magnesium Technology 2008, ed. M.O. Pekguleryuz, N.R. Neelameggham, R. Beals, and E.A. Nyberg (Warrendale: Minerals, Metals and Materials Society/AIME, 2008), Google Scholar
  22. 22.
    D. Furrer and J. Schirra, JOM 63, 42 (2011).CrossRefGoogle Scholar
  23. 23.
    M. Hillert, J. Alloys Compd. 320, 161 (2001).CrossRefGoogle Scholar
  24. 24.
    R.A. Otis and Z.K. Liu, J. Open Res. Softw. 5, 1 (2017).Google Scholar
  25. 25.
    M. Palumbo, B. Burton, A. e Silva, B. Fultz, B. Grabowski, G. Grimvall, B. Hallstedt, O. Hellman, B. Lindahl, A. Schneider, P.E.A. Turchi, and W. Xiong, Phys. Status Solidi B Basic Solid State Phys. 251, 14 (2014).CrossRefGoogle Scholar
  26. 26.
    C. Jiang, C. Wolverton, J. Sofo, L.Q. Chen, and Z.K. Liu, Phys. Rev. B 69, 214202 (2004).CrossRefGoogle Scholar
  27. 27.
    D. Shin, A. van de Walle, Y. Wang, and Z.K. Liu, Phys. Rev. B 76, 144204 (2007).CrossRefGoogle Scholar
  28. 28.
    A. van de Walle, P. Tiwary, M. de Jong, D.L. Olmsted, M. Asta, A. Dick, D. Shin, Y. Wang, L.Q. Chen, and Z.K. Liu, CALPHAD 42, 13 (2013).CrossRefGoogle Scholar
  29. 29.
    Y. Wang, C.L. Zacherl, S.L. Shang, L.Q. Chen, and Z.K. Liu, J. Phys. Condens. Matter 23, 485403 (2011).CrossRefGoogle Scholar
  30. 30.
    W. Cao, S.-L. Chen, F. Zhang, K. Wu, Y. Yang, Y.A. Chang, R. Schmid-Fetzer, and W.A. Oates, CALPHAD 33, 328 (2009).CrossRefGoogle Scholar
  31. 31.
    B. Jansson, Evaluation of Parameters in Thermochemical Models Using Different Types of Experimental Data Simultaneously (Stockholm: Royal Institute of Technology, 1984).Google Scholar
  32. 32.
    E. Königsberger and G. Eriksson, CALPHAD 19, 207 (1995).CrossRefGoogle Scholar
  33. 33.
    E. Königsberger, CALPHAD 15, 69 (1991).CrossRefGoogle Scholar
  34. 34.
    M. Stan and B.J.J. Reardon, CALPHAD 27, 319 (2003).CrossRefGoogle Scholar
  35. 35.
    H. Bozdogan, Psychometrika 52, 345 (1987).MathSciNetCrossRefGoogle Scholar
  36. 36.
    H. Akaike, Information Theory and an Extension of the Maximum Likelihood Principle, ed. E. Parzen, K. Tanabe, and G. Kitagawa (New York: Springer, 1998), pp. 199–213.Google Scholar
  37. 37.
    F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and É. Duchesnay, J. Mach. Learn. Res. 12, 2825 (2011).MathSciNetGoogle Scholar
  38. 38.
    D.S. Moore, G.P. McCabe, and B.A. Craig, Introduction to the Practice of Statistics (New York: W.H. Freeman and Company, 2014).Google Scholar
  39. 39.
    W.K. Hastings, Biometrika 57, 97 (1970).MathSciNetCrossRefGoogle Scholar
  40. 40.
    R.A. Otis, Ph.D. Dissertation, The Pennsylvania State University (2016).Google Scholar
  41. 41.
    N. Dupin, I. Ansara, and B. Sundman, CALPHAD 25, 279 (2001).CrossRefGoogle Scholar
  42. 42.
    X.L. Liu, G. Lindwall, T. Gheno, and Z.-K. Liu, CALPHAD 52, 125 (2016).CrossRefGoogle Scholar
  43. 43.
    Y. Wang, Z.K. Liu, and L.Q. Chen, Acta Mater. 52, 2665 (2004).CrossRefGoogle Scholar
  44. 44.
    R. Arroyave, D. Shin, and Z.K. Liu, Acta Mater. 53, 1809 (2005).CrossRefGoogle Scholar
  45. 45.
    C. Jiang, Acta Mater. 55, 4799 (2007).CrossRefGoogle Scholar
  46. 46.
    T. Wang, Ph.D. Dissertation, The Pennsylvania State University (2006).Google Scholar
  47. 47.
    X. Yuan, L. Zhang, Y. Du, W. Xiong, Y. Tang, A. Wang, and S. Liu, Mater. Chem. Phys. 135, 94 (2012).CrossRefGoogle Scholar
  48. 48.
    R.A. Otis, Z.K. Liu, The Jupyter Notebook for the Parameter Evaluation in the Al-Ni Binary System (2016). https://github.com/richardotis/pycalphad-fitting/blob/adb39d7123b0b151d67910d51edd2182c8d9727e/Parameters.ipynb. Accessed 22 March 2017.
  49. 49.
    C. Jiang, L.Q. Chen, and Z.K. Liu, Acta Mater. 53, 2643 (2005).CrossRefGoogle Scholar
  50. 50.
    J.O. Andersson, T. Helander, L.H. Hoglund, P.F. Shi, and B. Sundman, CALPHAD 26, 273 (2002).CrossRefGoogle Scholar

Copyright information

© The Minerals, Metals & Materials Society 2017

Authors and Affiliations

  1. 1.Department of Materials Science and EngineeringThe Pennsylvania State UniversityUniversity ParkUSA
  2. 2.Engineering and Science Directorate, Jet Propulsion LaboratoryCalifornia Institute of TechnologyPasadenaUSA

Personalised recommendations