Speaker
Description
Prosperity and lifestyle of our society largely rely on improved or even novel materials that make new products possible for the energy, environment, health, mobility, IT sectors, and more. Materials Science has been producing for decades a huge amount of possibly extremely valuable data, which, however, are seldom re-used or even accessed outside the single project they were produced for. Besides, data that are not used for a publication are simply forgotten and wasted. In order to turn this gold mine of data into value, FAIR (Findable, Accessible, Interoperable, and Re-usable) data infrastructures are necessary. Even more, data need to be ready to be harvested, explored, and analyzed by means of artificial intelligence. In this sense, our forward-looking re-interpretation of the FAIR acronym is that research data should be “Findable and AI-Ready”.
The FAIRmat consortium of the German Research-Data Infrastructure (NFDI) has been established in October 2021 with the mission of implementing a FAIR research-data infrastructure that interweaves data and tools from and for materials synthesis, experiment, theory, and computation [1, 2].
FAIRmat builds upon and expands the achievements of the first phase of the NOMAD Center of Excellence, which had established a FAIR data infrastructure for ab initio computational materials-science. The core of the infrastructure is the development and deployment of a hierarchical, modular, and extensible metadata schema, which maps the information contained in atomistic-simulation codes, stored in the NOMAD Repository, into a standardized representation, stored in the NOMAD Archive. The content of the Archive is accessed via a flexible API and can be browsed in the NOMAD Encyclopedia or analyzed with AI tools in the NOMAD AI toolkit. The NOMAD infrastructure, which is web based and operable without registration, has also a local counterpart, the NOMAD Oasis, which allows users to operate with exactly the same tools and interfaces on local, private data, also behind strong fire walls.
In this three-panel poster, we present the current results together with established achievements in the NOMAD Infrastructure and FAIRmat consortium. In particular, the challenge of representing complex workflows from synthesis, experiments, and computation is addressed and a roadmap is provided.
References
[1] C. Draxl and M. Scheffer, Big-Data-Driven Materials Science and its FAIR Data Infrastructure. Plenary Chapter in Handbook of Materials Modeling (eds. S. Yip and W. Andreoni), Springer (2020). https://doi.org/10.1007/978-3-319-44677-6_104
[2] M. Scheffler, et al. and C. Draxl, FAIR data enabling new horizons for materials research. Nature 604, 635 (2022). https://doi.org/10.1038/s41586-022-04501-x
Addresses
(a) Physics Department and IRIS Adlershof, Humboldt-Universität zu Berlin, Berlin, Germany
(b) The FAIRmat Consortium of the NFDI: https://www.fair-di.eu/fairmat/fairmat_/consortium
| Abstract Number (department-wise) | SG 01 |
|---|---|
| Department | Scheffler Group |
