Check nearby libraries
Buy this book
![Loading indicator](/images/ajax-loader-bar.gif)
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.
Check nearby libraries
Buy this book
![Loading indicator](/images/ajax-loader-bar.gif)
Subjects
Data mining, Crystals, Structure, Materials, ResearchShowing 1 featured edition. View all 1 editions?
Edition | Availability |
---|---|
1
Data mining-aided crystal engineering for the design of transparent conducting oxides: preprint
2010, National Renewable Energy Laboratory
electronic resource :
in English
|
aaaa
Libraries near you:
WorldCat
|
Book Details
Edition 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.
The Physical Object
ID Numbers
Source records
Community Reviews (0)
Feedback?December 13, 2022 | Created by MARC Bot | import new book |