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Data-Mining-Driven Quantum Mechanics for the Prediction of Structure


The prediction of crystal structure is a key outstanding problem in materials science and one that is fundamental to computational materials design. We argue that by combining the predictive accuracy of quantum mechanics with data mining tools to extract knowledge from a large body of historical experimental or computational results, this problem can be successfully addressed.

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1 A. Van de Walle and G. Ceder , J. Phase Equilib. 23 (2002) p.348.

, and () p..6 S. Curtarolo D. Morgan G. Ceder Calphad 29 2005 163

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9 G.H. Johannesson , T. Bligaard , A.V. Ruban , H.L. Skriver , K.W. Jacobsen , and J.K. Norskøv , Phys. Rev. Lett. 88 255506 (2002).

10 M. Jansen Angew. Chem. Int. Ed. 41 (2002) p.3746.

11 D. Morgan , G. Ceder and S. Curtarolo Meas. Sci. Technol. 16 (2005) p.296.

12 C. Fischer , K. Tibbetts , D. Morgan , and G. Ceder Nature Mater. 5 (2006) p.641.

15 D.G. Pettifor J.Phys. C: Solid State Physics 19 (1986) p.285.

18 D. Morgan and G. Ceder in The Handbook of Materials Modeling, edited by S. Yip (Springer, New York, 2005) p.395.

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MRS Bulletin
  • ISSN: 0883-7694
  • EISSN: 1938-1425
  • URL: /core/journals/mrs-bulletin
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