Big Semantic Data Processing in the Materials Design Domain

Lambrix, Patrick; Armiento, Rickard; Delin, Anna; Li, Huanyu

doi:10.1007/978-3-319-77525-8_293

Big Semantic Data Processing in the Materials Design Domain

Patrick Lambrix³,
Rickard Armiento³,
Anna Delin⁴ &
Huanyu Li³

Reference work entry
First Online: 20 February 2019

49 Accesses

Definitions

To speed up the progress in the field of materials design, a number of challenges related to big data need to be addressed. This entry discusses these challenges and shows the semantic technologies that alleviate the problems related to variety, variability, and veracity.

Overview

Materials design and materials informatics are central for technological progress, not the least in the green engineering domain. Many traditional materials contain toxic or critical raw materials, whose use should be avoided or eliminated. Also, there is an urgent need to develop new environmentally friendly energy technology. Presently, relevant examples of materials design challenges include energy storage, solar cells, thermoelectrics, and magnetic transport (Ceder and Persson 2013; Jain et al. 2013; Curtarolo et al. 2013).

The space of potentially useful materials yet to be discovered – the so-called chemical white space– is immense. The possible combinations of, say, up to six different...

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Authors and Affiliations

Linköping University, Swedish e-Science Research Centre, Linköping, Sweden
Patrick Lambrix, Rickard Armiento & Huanyu Li
Royal Institute of Technology, Swedish e-Science Research Centre, Stockholm, Sweden
Anna Delin

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Patrick Lambrix
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Rickard Armiento
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Anna Delin
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Huanyu Li
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Correspondence to Patrick Lambrix .

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Editors and Affiliations

Institute of Computer Science, University of Tartu, Tartu, Estonia
Sherif Sakr
School of Information Technologies, Sydney University, Sydney, Australia
Albert Y. Zomaya

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Lambrix, P., Armiento, R., Delin, A., Li, H. (2019). Big Semantic Data Processing in the Materials Design Domain. In: Sakr, S., Zomaya, A.Y. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-77525-8_293

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DOI: https://doi.org/10.1007/978-3-319-77525-8_293
Published: 20 February 2019
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-77524-1
Online ISBN: 978-3-319-77525-8
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