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Big Semantic Data Processing in the Materials Design Domain

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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.

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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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  • DOI: 10.1007/978-3-319-77525-8_293
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Correspondence to Patrick Lambrix .

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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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