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NOMAD: The FAIR concept for big data-driven materials science

Published online by Cambridge University Press:  10 September 2018

Claudia Draxl  and
Matthias Scheffler
Claudia Draxl
Affiliation:
Humboldt-Universität zu Berlin, and Fritz-Haber-Institut Berlin, Germany; claudia.draxl@physik.hu-berlin.de
Matthias Scheffler
Affiliation:
Fritz-Haber-Institut Berlin, and Humboldt-Universität zu Berlin, Germany; scheffler@fhi-berlin.mpg.de
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Abstract

Data are a crucial raw material of this century. The amount of data that have been created in materials science thus far and that continues to be created every day is immense. Without a proper infrastructure that allows for collecting and sharing data, the envisioned success of big data-driven materials science will be hampered. For the field of computational materials science, the NOMAD (Novel Materials Discovery) Center of Excellence (CoE) has changed the scientific culture toward comprehensive and findable, accessible, interoperable, and reusable (FAIR) data, opening new avenues for mining materials science big data. Novel data-analytics concepts and tools turn data into knowledge and help in the prediction of new materials and in the identification of new properties of already known materials.

Keywords

data repositoriesmetadatadata sharingmachine learningartificial intelligence

Information

Type
Data-Centric Science for Materials Innovation
Information
MRS Bulletin , Volume 43 , Issue 9: Data-Centric Science for Materials Innovation , September 2018 , pp. 676 - 682
Copyright
Copyright © Materials Research Society 2018 

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