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Data Mining-Aided Crystal Engineering for the Design of Transparent Conducting Oxides

Published online by Cambridge University Press:  08 September 2011

Changwon Suh ,
Kwiseon Kim ,
Joseph J. Berry ,
Jinsuk Lee  and
Wesley B. Jones
Changwon Suh
Affiliation:
National Renewable Energy Laboratory, 1617 Cole Blvd., Golden, CO 80401, U.S.A.
Kwiseon Kim
Affiliation:
National Renewable Energy Laboratory, 1617 Cole Blvd., Golden, CO 80401, U.S.A.
Joseph J. Berry
Affiliation:
National Renewable Energy Laboratory, 1617 Cole Blvd., Golden, CO 80401, U.S.A.
Jinsuk Lee
Affiliation:
National Renewable Energy Laboratory, 1617 Cole Blvd., Golden, CO 80401, U.S.A.
Wesley B. Jones
Affiliation:
National Renewable Energy Laboratory, 1617 Cole Blvd., Golden, CO 80401, U.S.A.
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Abstract

The purpose of this paper is to accelerate the pace of material discovery processes by systematically visualizing the huge search space that conventionally needs to be explored. To this end, we demonstrate not only the use of empirical- or crystal chemistry-based physical intuition for decision-making, but also to utilize knowledge-based data mining methodologies in the context of finding p-type delafossite transparent conducting oxides (TCOs). We report on examples using high-dimensional visualizations such as radial visualization combined with machine learning algorithms such as k-nearest neighbor algorithm (k-NN) to better define and visualize the search space (i.e. structure maps) of functional materials design. The vital role of search space generated from these approaches is discussed in the context of crystal chemistry of delafossite crystal structure.

Keywords

transparent conductorcrystalstructural
Type
Research Article
Information
MRS Online Proceedings Library (OPL) , Volume 1315: Symposium MM – Transparent Conducting Oxides and Applications , 2011 , mrsf10-1315-mm02-07
Copyright
Copyright © Materials Research Society 2011

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