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Predicting crystal structure by merging data mining with quantum mechanics


Modern methods of quantum mechanics have proved to be effective tools to understand and even predict materials properties. An essential element of the materials design process, relevant to both new materials and the optimization of existing ones, is knowing which crystal structures will form in an alloy system. Crystal structure can only be predicted effectively with quantum mechanics if an algorithm to direct the search through the large space of possible structures is found. We present a new approach to the prediction of structure that rigorously mines correlations embodied within experimental data and uses them to direct quantum mechanical techniques efficiently towards the stable crystal structure of materials.

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Figure 1: The Fe3C and MgCu2 structure types.
Figure 2: Mutual information between pairs of variables.
Figure 3: Predicting the structure of AgMg3.
Figure 4: Large-scale prediction capability.


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This work was funded by NSF-ITR grant DMR-0312537, the Institute for Soldier Nanotechnologies (ISN) grant DAAD 19-02-D-0002, and the DOE, Office of Basic Energy Sciences under Contract No. DE-FG02-96ER45571. We would also like to acknowledge the National Science Foundation and the San Diego Supercomputer Center for additional computational resources.

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Correspondence to Gerbrand Ceder.

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Fischer, C., Tibbetts, K., Morgan, D. et al. Predicting crystal structure by merging data mining with quantum mechanics. Nature Mater 5, 641–646 (2006).

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