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Computational crystal structure prediction with high-through-put Ab initio and data mining methods

Publication ,  Journal Article
Morgan, D; Ceder, G; Curtarolo, S
Published in: JOM
2004

Crystal structure prediction is an essential step in rational materials design. Unfortunately, there is no general tool for reliably predicting crystal structures of new alloys. Total energy ab initio approaches can be used to accurately compare energies of different candidate structures, but developing a manageable list of candidate structures for comparison is still very challenging. A powerful new tool to tackle this problem is "high-throughput" ab initio computation, which makes use of robust automated techniques to perform many thousands of calculations. High-throughput ab initio can be enhanced with data mining techniques,which can be used to accelerate structure prediction in new alloys. We have used high-throughput methods to calculate over 14,000 full ab initio structural optimizations on 80 intermetallic binary alloys, and implemented a novel data mining scheme that shows potential to dramatically reduce the time necessary for identify stable cry structures in new alloys.

Duke Scholars

Published In

JOM

Publication Date

2004

Volume

56

Issue

11

Start / End Page

70

Related Subject Headings

  • Materials
  • 0914 Resources Engineering and Extractive Metallurgy
  • 0913 Mechanical Engineering
  • 0912 Materials Engineering
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Morgan, Dane, Gerbrand Ceder, and Stefano Curtarolo. “Computational crystal structure prediction with high-through-put Ab initio and data mining methods.” JOM 56, no. 11 (2004): 70.

Published In

JOM

Publication Date

2004

Volume

56

Issue

11

Start / End Page

70

Related Subject Headings

  • Materials
  • 0914 Resources Engineering and Extractive Metallurgy
  • 0913 Mechanical Engineering
  • 0912 Materials Engineering