Skip to main content
SpringerLink
Log in

The materials genome and CALPHAD

  • Progress
  • Materials Science
  • Published:
Chinese Science Bulletin

Abstract

The mapping of the human genome is an important basis for the development of new medicals and medical treatments. Consequently, it has attracted tremendous research funding over the last decade. On June 2011, the Materials Genome Initiative was announced by the US President Obama as collaboration on modeling and advanced materials databases. Unfortunately, the materials genome was given a rather vague definition in the announcement. However, the materials genome should be defined in analogy with biological genomes and one may then conclude that: at any moment, the performance of a specific material depends on its chemical composition (inherent property stored in its genome) and its environment (external interactions–processing–conditions during usage). The materials genome should thus be defined as a set of information encoded in the language of thermodynamics obtained by careful assessment of experimental data and quantum mechanical calculations from which certain conclusions about the material can be drawn. The CALPHAD databases contain the thermodynamic and kinetic properties of a materials system. Such databases allow the prediction of materials structure as well as its response to processing and usage conditions, and are major parts of integrated computational materials engineering.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Kaufman L, Bernstein H (1970) Computer calculation of phase diagrams. Academic Press, New York

    Google Scholar 

  2. Saunders N, Miodownik AP (1998) CALPHAD—a comprehensive guide. Pergamon Press, Oxford

    Google Scholar 

  3. National Research Council (2004) Accelerating technology transition—bridging the valley of death for materials and processes in defense systems. The National Academies Press, Washington DC

    Google Scholar 

  4. National Research Council (2008) Integrated computational materials engineering: a transformational discipline for improved competitiveness and national security. The National Academies Press, Washington DC

    Google Scholar 

  5. Stokes DE (1997) Pasteur’s quadrant—basic science and technological innovation. Brookings Institution Press, Washington DC

    Google Scholar 

  6. Borgenstam A, Höglund L, Ågren J et al (2000) DICTRA, a tool for simulation of diffusional transformation in alloys. J Phase Equilib 21:269–280

    Article  Google Scholar 

  7. Villanueva W, Grönhagen K, Amberg G et al (2008) Multicomponent and multiphase modeling and simulation of reactive wetting. Phys Rev E 77:056313

    Article  Google Scholar 

  8. Yeddu KH, Malik A, Ågren J et al (2012) Three-dimensional phase-field modeling of martensitic microstructure evolution in steels. Acta Mater 60:1538–1547

    Article  Google Scholar 

  9. Calcagnotto M, Ponge D, Demir E et al (2010) Orientation gradients and geometrically necessary dislocations in ultrafine grained dual-phase steels studies by 2D and 3D EBSD. Mater Sci Eng A 527:2738–2746

    Article  Google Scholar 

  10. Liss KL, Bartels A, Schreyer A et al (2003) High-energy X-rays: a tool for advanced bulk investigations in materials science and physics. Text Microstruct 35:219–252

    Article  Google Scholar 

  11. Miller MK, Forbes RG (2009) Atom probe tomography. Mater Charact 60:461–469

    Article  Google Scholar 

  12. Gibbs JW (1948) Collected works. Yale University Press, New Haven

    Google Scholar 

  13. van Laar JJ (1908) Melting or solidification curves in binary system. Z Phys Chem 63:216

    Google Scholar 

  14. Kubaschewski O, Evans ELL, Alcock CB (1967) Metallurgical thermochemistry. Pergamon Press, Oxford

    Google Scholar 

  15. Daniel RS (1965) JANAF thermochemical tables. Clearinghouse for federal scientific and technical information, Dow Chemical Company

    Google Scholar 

  16. Barin I, Knacke O, Kubaschewski O (1977) Themochemical properties of inorganic substances. Springer, Berlin

    Book  Google Scholar 

  17. Hultgren R, Desai PD, Hawkins DT et al (1973) Selected values of the thermodynamic properties of the elements. American Society for Metals, Metals Park

    Google Scholar 

  18. Hultgren R, Desai PD, Hawkins DT et al (1973) Selected values of the thermodynamic properties of binary alloys. American Society for Metals, Metals Park

    Google Scholar 

  19. Eriksson G (1975) Thermodynamic studies of high temperature equilibria. Chem Scr 8:100–103

    Google Scholar 

  20. Lukas HL, Henig ET, Zimmermann B (1977) Optimization of phase diagrams by a least squares method using simultaneously different types of data. Calphad Comput Coupling Ph Diagr Thermochem 1:225–236

    Article  Google Scholar 

  21. Evans LB, Boston JF, Britt HI et al (1979) ASPEN: an advanced system for process engineering. Comput Chem Eng 3:319–327

    Article  Google Scholar 

  22. Pelton AD, Bale CW (1977) Computational techniques for the treatment of thermodynamic data in multicomponent systems and the calculation of phase equilibria. Calphad Comput Coupling Ph Diagr Thermochem 1:253–273

    Article  Google Scholar 

  23. Sundman B, Jansson B, Andersson JO (1985) The thermo-calc databank system. Calphad Comput Coupling Ph Diagr Thermochem 9:153–190

    Article  Google Scholar 

  24. Davies RH, Dinsdale AT, Gisby JA et al (2002) MTDATA—thermodynamic and phase equilibrium software from the national physical laboratory. Calphad Comput Coupling Ph Diagr Thermochem 26:229–271

    Article  Google Scholar 

  25. Andersson JO, Ågren J (1992) Models for numerical treatment of multicomponent diffusion in simple phases. J Appl Phys 72:1350–1355

    Article  Google Scholar 

  26. Campbell CE, Boettinger WJ, Kattner UR (2002) Development of a diffusion mobility database for Ni-base superalloys. Acta Mater 50:775–792

    Article  Google Scholar 

  27. Razumoskiy VI, Ruban AV, Korzhavyi PA (2011) Effect of temperature on the elastic anisotropy of pure Fe and Fe0.9Cr0.1 random alloy. Phys Rev Lett 107:205504

    Article  Google Scholar 

  28. Andersson DA, Simak SI, Skorodumova NV et al (2006) Optimization of ionic conductivity in doped ceria. Proc Natl Acad Sci USA 103:3518–3521

    Article  Google Scholar 

  29. Xiong W, Hedström P, Selleby M et al (2011) An improved thermodynamic modeling of the Fe–Cr system down to zero Kelvin coupled with key experiments. Calphad Comput Coupling Ph Diagr Thermochem 35:355–366

    Article  Google Scholar 

Download references

Acknowledgments

The author would like to thank Professor Malin Selleby and associate Professors Joakim Odqvist and Annika Borgenstam from the Royal Institute of Technology for valuable suggestions. Funding within the Hero-m project supported by VINNOVA (the Swedish Governmental Agency for Innovation Systems), KTH, and Swedish industry is gratefully acknowledged.

Author information

Authors and Affiliations

  1. Materials Science and Engineering, The Royal Institute of Technology, 100 44, Stockholm, Sweden

    John Ågren

Authors
  1. John Ågren
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to John Ågren.

Additional information

SPECIAL ISSUE: Materials Genome

About this article

Cite this article

Ågren, J. The materials genome and CALPHAD. Chin. Sci. Bull. 59, 1635–1640 (2014). https://doi.org/10.1007/s11434-013-0108-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11434-013-0108-2

Keywords

Search

Navigation