Christopher Carl Fischer

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0927-0256/$ see front matter 2011 Elsevier B.V. A doi:10.1016/j.commatsci.2011.02.023 ⇑ Corresponding author. Tel.: +1 617 253 1581; fax E-mail address: gceder@mit.edu (G. Ceder). The use of high-throughput density functional theory (DFT) calculations to screen for new materials and conduct fundamental research presents an exciting opportunity for materials(More)
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(More)
This thesis develops a machine learning framework for predicting crystal structure and applies it to binary metallic alloys. As computational materials science turns a promising eye towards design, routine encounters with chemistries and compositions lacking experimental information will demand a practical solution to structure prediction. We review the(More)
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