Data mining-aided crystal engineering for the design of transparent conducting oxides
Subtitle: preprint
By: Changwon Suh ... [et al.].
Contributors: Materials Research Society. Meeting; Suh, Changwon
Series: NREL/CP -- 2C00-50079; Conference paper (National Renewable Energy Laboratory (U.S.)) -- NREL/CP-2 C 00-50079.
Publisher: National Renewable Energy Laboratory
Published: 2010
Place of publication: Golden, CO]
Language: eng
Format: [electronic resource] :
Subjects: Data mining; Crystals; Structure; Materials; Research
Description: 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.
Notes: Title from title screen (viewed January 24, 2011).

"December 2010."

"To be presented at the Materials Research Society Fall Meeting, Boston, Massachusetts, November 29-December 3, 2010."

Includes bibliographical references (p. 6-7).

DE-AC36--08GO28308 PVA9.2910

Full text available via Internet in .pdf format. Adobe Acrobat Reader required.