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Data Mining Approach to Ab-Initio Prediction of Crystal Structure

Published online by Cambridge University Press:  01 February 2011

Dane Morgan ,
Gerbrand Ceder  and
Stefano Curtarolo
Dane Morgan
Affiliation:
Department of Materials Science and Engineering, Massachusetts Institute of Technology Cambridge, Massachusetts 02139, USA
Gerbrand Ceder
Affiliation:
Department of Materials Science and Engineering, Massachusetts Institute of Technology Cambridge, Massachusetts 02139, USA
Stefano Curtarolo
Affiliation:
Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC 27708
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Abstract

Predicting crystal structure is one of the most fundamental problems in materials science and a key early step in computational materials design. Ab initio simulation methods are a powerful tool for predicting crystal structure, but are too slow to explore the extremely large space of possible structures for new alloys. Here we describe ongoing work on a novel method (Data Mining of Quantum Calculations, or DMQC) that applies data mining techniques to existing ab initio data in order to increase the efficiency of crystal structure prediction for new alloys. We find about a factor of three speedup in ab intio prediction of crystal structures using DMQC as compared to naïve random guessing. This study represents an extension of work done by Curtarolo, et al. [1] to a larger library of data.

Type
Research Article
Information
MRS Online Proceedings Library (OPL) , Volume 804: Symposium JJ – Combinatorial and Artificial Intelligence Methods in Materials Science II , 2003 , JJ9.25
Copyright
Copyright © Materials Research Society 2004

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References

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