Description
AI applications in materials science and catalysis increasingly depend on access to large, well-structured and semantically meaningful experimental datasets. In this contribution, we will discuss how FAIRmat addresses these challenges through community-agreed data standards, semantic data models, and the NOMAD ecosystem.1,2 Examples from activities at the MPI for Chemical Energy Conversion (MPI CEC) will illustrate these approaches. A particular focus will be on transforming heterogeneous experimental data into interoperable datasets that can be used for AI training and other machine-learning applications. We will present examples from experimental materials-science domains, including spectroscopy and microscopy, and discuss how NOMAD supports the generation, contextualization, and reuse of AI-ready data. We will also outline planned developments for FAIRmat’s second funding period, including the integration of AI workflows with the FAIRmat/NOMAD ecosystem and planned AI applications at MPI CEC that build on the resulting data models and AI infrastructure.
(1) Scheidgen et al., (2023). NOMAD: A distributed web-based platform for managing materials science research data. Journal of Open Source Software, 8(90), 5388, https://doi.org/10.21105/joss.05388
(2) Schumann, J.; Näsström, H.; Götte, M.; Himanen, L.; Moshantaf, A.; Scheidgen, M.; Márquez, J. A.; Draxl, C.; Trunschke, A. Enabling open and FAIR catalysis data with standardized data structures. Nature Catalysis 2026, 9 (3), 225-229. https://doi.org/10.1038/s41929-026-01508-9.
| MPI | MPI for Chemical Energy Conversion |
|---|---|
| NFDI Consortium | FAIRmat |