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

Suh, C; Kim, K; Berry, J J; Lee, J; Jones, W B

doi:10.1557/opl.2011.1387

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

Conference · Wed Dec 01 04:00:00 EST 2010

DOI:https://doi.org/10.1557/opl.2011.1387· OSTI ID:1001448

Suh, C; Kim, K; Berry, J J; Lee, J; Jones, W B

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-typedelafossite 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 isdiscussed in the context of crystal chemistry of delafossite crystal structure.

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Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE)

DOE Contract Number:: AC36-08GO28308

OSTI ID:: 1001448

Report Number(s):: NREL/CP-2C00-50079

Country of Publication:: United States

Language:: English

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

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