- Morgan, D. and Ceder, G. and Curtarolo, S., Data mining approach to ab initio prediction of crystal structure,
Combinatorial and Artificial Intelligence Methods in Materials Science II Symposium (Mater. Res. Soc. Symposium Proceedings Vol.804)
pp. 343 - 8 .
(last updated on 2007/04/06)
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 initio prediction of crystal structures using DMQC as compared to naive random guessing. This study represents an extension of work done by Curtarolo, et al. to a larger library of data
ab initio calculations;alloys;crystal structure;data mining;