FAIR research data infrastructure and its importance for data driven materials science – the role of FAIRmat

Walid Hetaba, Max-Planck-Institut für Chemische Energiekonversion, Germany

A vast amount of research data is generated every day using experimental and computational methods. These data provide a rich feedstock that can be harnessed for the development of new materials in physics and chemistry. However, the generated data can only be shared, explored, and used if it is made available in a comprehensive and comprehensible way. The NFDI consortium FAIRmat aims to provide such a research data infrastructure according to the FAIR principles Findable, Accessible, Interoperable and Reusable. In FAIRmat the development of methods for the complete characterization of research data is split to the areas of synthesis, experiments, and theory and computations. A common data infrastructure combines the methods of the different areas and standardized workflows for materials synthesis, experiments and computations are being developed. FAIRmat can make data Findable and AI Ready and thus serve as the basis for data driven materials science. I will give an overview on FAIRmat, its current status, and outlook to further developments. In addition, I will show an example how the current developments within FAIRmat are linked to our group’s efforts on automated data analysis using convolutional neural networks.

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