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

By C. Draxl, Matthias Scheffler. MRS bulletin, 2018

397 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Materials Chemistry, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/fff51615943e08d05080682009c9c656321ef0b2

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Citations in the corpus

SISSO: A compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates (2017, methodology): “This type of complexity is a significant element of ‘the fourth paradigm in materials science’ [18, 29, 30] which recognizes that it may not be possible to describe many properties of functional materials by a single, physically founded model, i.”
Recent advances and applications of deep learning methods in materials science (2021, neutral): “eu/prod/rae/gui/search [10] Open catalys projectP 1.”