Journal
MRS BULLETIN
Volume 43, Issue 9, Pages 676-682Publisher
SPRINGER HEIDELBERG
DOI: 10.1557/mrs.2018.208
Keywords
data repositories; metadata; data sharing; machine learning; artificial intelligence
Funding
- EU's Horizon 2020 Research and Innovation Programme [676580]
- NOMAD Laboratory CoE
- ERC: TEC1P [740233]
- Einstein Foundation Berlin
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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.
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