The NOMAD laboratory: from data sharing to artificial intelligence

By C. Draxl, Matthias Scheffler. Journal of Physics Materials, 2019

348 citations recorded by the source corpus.

The Novel Materials Discovery (NOMAD) Laboratory is a user-driven platform for sharing and exploiting computational materials science data. It accounts for the various aspects of data being a crucial raw material and most relevant to accelerate materials research and engineering. NOMAD, with the NOMAD Repository, and its code-independent and normalized form, the NOMAD Archive, comprises the worldwide largest data collection of this field. Based on its findable accessible, interoperable, reusable data infrastructure, various services are offered, comprising advanced visualization, the NOMAD Encyclopedia, and artificial-intelligence tools. The latter are realized in the NOMAD Analytics Toolkit. Prerequisite for all this is the NOMAD metadata, a unique and thorough description of the data, that are produced by all important computer codes of the community. Uploaded data are tagged by a persistent identifier, and users can also request a digital object identifier to make data citable. Developments and advancements of parsers and metadata are organized jointly with users and code developers. In this work, we review the NOMAD concept and implementation, highlight its orthogonality to and synergistic interplay with other data collections, and provide an outlook regarding ongoing and future developments.

Subfields: Materials Chemistry, Surfaces, Coatings and Films

View Paper: https://www.semanticscholar.org/paper/836eedb1f4f3f4bd6d9c7b77045ca74166417b29

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In Materials Chemistry

Citations in the corpus

Scaling deep learning for materials discovery (2023, methodology): “Instead, computational approaches championed by the Materials Project (MP) 16 , the Open Quantum Materials Database (OQMD) 17 , AFLOWLIB 20 and NOMAD 21 have used first-principles calculations based on density functional theory (DFT) as approximations of physical energies.”
The Role of Machine Learning in the Understanding and Design of Materials (2020, background): “For instance, the Materials Project, 32 the Pauling File, 33 Novel Materials Discovery (NOMAD), 34 and Materials Cloud 35 contain computational materials data.”