Materials databases for the computational materials scientist

Abstract

Until recently, many computational materials scientists have shown little interest in materials databases. This is now changing because the amount of computational data is rapidly increasing and the potential for data mining provides unique opportunities for discovery and optimization. Here, a few examples of such opportunities are discussed relating to structural analysis and classification, discovery of correlations between materials properties, and discovery of unsuspected compounds.

Keywords

database systems / materials / data mining / ab initio prediction / structural analysis

References

[1] Meschel S.V. Numeric databases in the sciences. Online Rev., 1984, 8(1): 77.

[2] Jolliffe I.T. Principal Component Analysis (Series: Springer Series in Statistics), 2002 2nd ed. NY, Springer, 1.

[3] Simonovic D., Sluiter M.H.F. Impurity diffusion activation energies in Al from first principles. Phys. Rev. B, 2009, 79, 054304.

[4] M.H.F. Sluiter, Compilation of “1st principles” Formation Enthalpy Data, 1996, http://www-lab.imr.edu/~marcel/enthalpy/enthlp.html.

[5] Y. Chen, Computational Electronic Structure Database (CompES), 1996–2002, http://caldb.nims.go.jp/index_en.html.

[6] M. Mihalkovic and M. Widom, Alloy Database, 2004–2009, http://alloy.phys.cmu.edu/index.html.

[7] Wang Y., Curtarolo S., Jiang C., et al. Ab initio lattice stability in comparison with CALPHAD lattice stability. Calphad, 2004, 28, 79.

[8] Sluiter M.H.F. Ab initio lattice stabilities of some elemental complex structures. Calphad, 2006, 30, 357.

[9] Sluiter M.H.F. Lattice stability prediction of elemental tetrahedrally close-packed structures. Acta Mater., 2007, 55, 3707.

[10] Fischer C.C., Tibbetts K.J., Morgan D., Ceder G. Predicting crystal structure by merging data mining with quantum mechanics. Nat. Mater., 2006, 5, 641.

[11] Maddox J. Crystals from first principles. Nature, 1988, 335, 201.

[12] Oganov A.R., Glass C.W. Crystal structure prediction using ab initio evolutionary techniques: principles and applications. J. Chem. Phys., 2006, 124, 244704.

[13] Trimarchi G., Freeman A.J., Zunger Alex Predicting stable stoichiometries of compounds via evolutionary global space-group optimization. Phys. Rev. B, 2009, 80, 092101.

[14] Villars P., Cenzual K., Daams J.L.C., et al. Pauling File: Inorganic Materials Database and Design System, Binaries Edition, 2003 Metal Park, OH, ASM International

[15] Tasci E., Sluiter M.H.F., Pasturel A., Villars P. Liquid structure as a guide for phase stability in the solid state: Discovery of a stable compound in the Au-Si alloy system. Acta Mater., 2010, 58, 449.

[16] Tasci E.S., Sluiter M.H.F., Pasturel A., Jakse N. Existence of a stable compound in the Au-Ge alloy system. Phys. Rev. B, 2010, 81, 172202.

[17] Villars P., Daams J.L.C. Atomic-environment classification of the chemical elements. J. Alloys Compd., 1993, 197, 177.

[18] Sluiter M.H.F. Some observed bcc, fcc, and hcp superstructures. Phase Transitions, 2007, 80, 299.

[19] Massalski T.B. Binary Alloy Phase Diagrams, 1990 2nd ed. Materials Park, OH, ASM International

[20] Chen H.S., Turnbull D. Thermal properties of gold-silicon binary alloy near the eutectic composition. J. Appl. Phys., 1967, 38, 3646.

[21] Pettifor D.G. Structure maps revisited. J. Phys. Condens. Matter, 2003, 15, V13.