Data mining approach to ab-initio prediction of crystal structure
Published
Journal Article
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.
Full Text
Duke Authors
Cited Authors
- Morgan, D; Ceder, G; Curtarolo, S
Published Date
- January 1, 2003
Published In
Volume / Issue
- 804 /
Start / End Page
- 343 - 348
International Standard Serial Number (ISSN)
- 0272-9172
Digital Object Identifier (DOI)
- 10.1557/proc-804-jj9.25
Citation Source
- Scopus