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SYFD: Symposium Pushing the Boundaries of Fair Data Practices for Condensed Matter Insights: From Workflows to Machine Learning
SYFD 1: Pushing the Boundaries of Fair Data Practices for Condensed Matter Insights
SYFD 1.2: Hauptvortrag
Mittwoch, 19. März 2025, 10:00–10:30, H1
Establishing Workflows of Experimental Solar Cell Data into NOMAD — Edgar Nandayapa1, Paolo Graniero1, Jose Marquez2, Michael Götte1, and •Eva Unger1,3 — 1Helmholtz-Zentrum Berlin für Materialen und Energie GmbH, HySPRINT Innovation Lab, Kekuléstraße 5, 12489 Berlin, Germany — 2Humboldt University Berlin, FAIRmat Project, Zum Großen Windkanal 2, 12489 Berlin, Germany — 3Humboldt University Berlin, Department of Chemistry and CSMB, Zum Großen Windkanal 2, 12489 Berlin, Germany
Materials for solar energy conversion are key enablers for the green energy transition. Perovskite Solar Cells (PSCs) are an excellent example of an emerging technology, where an intense and world-wide R&D activities has enabled a very fast improvement in the reported power conversion efficiencies. The monthly output of new reports in the peer-reviewed literature is in the hundreds to thousands and it is at this point neither effective nor possible to still make effcient use of the research results reported. Considering data reported in the peer-reviewed literature, this just represent the “tip of the iceberg” of research data that is actually being measured in labs around the world.
Out of desparation, our team started a fairly manual “data digging” initiative in 2019 to compile data from the then published data in the peer-reviewed literature into a single database based on a very rudimentary and single-metric representation of the actual research data resulting in the Perovskite Database (www.perovskitedatabase.com). In close collaboration with the FAIRmat project, we are now taking steps towards transferring the literature dataset into NOMAD and are creating NOMAD platforms to capture, store, analyse and share the actual experimental research data within and beyond our research community. The goal is to, both, initiate community driven data sharing platforms that can be used to directly share and disseminate experimental datasets to adhere to FAIR data principles, and make photovoltaic research data AI-ready to enable the utilization of modern ML-tools to facilitate a further acceleration of the technological exploitation of new materials.