The evolution of new solar cell technologies is often slow-paced, typically requiring decades to surpass the 20% power conversion efficiency barrier for commercial viability. Given the almost infinite number of potential chemical compositions for new absorber layer materials and the limitless possibilities of device architectures, navigating this material space becomes an unattainable task without employing data science tools.

This talk will introduce the NOMAD Laboratory (https://nomad-lab.eu), a platform supported by the NFDI consortium FAIRmat (https://fairmat-nfdi.eu), designed to meet these challenges head-on by making materials science data Findable, Accessible, Interoperable, and Reusable (FAIR). I will illustrate the ongoing evolution of the NOMAD infrastructure in supporting solar cell research, featuring an app developed to visualize and search an expansive and AI-ready solar cell dataset. The platform also includes an adaptable electronic lab notebook (ELN) that can be tailored by research labs to facilitate AI-ready data/metadata capture, transfer, and processing within a FAIR database context. Building upon the public data available in NOMAD, enriched by the perovskite database project, I will discuss the evolution of the materials used as transport layers in perovskite solar cells based on network analysis. This study highlights the need for our current scientific publication culture to be supplemented with structured data artifacts. These additional resources can enable scientists to better leverage the vast knowledge presented in the rapidly expanding body of publications in their discipline, which is currently an overwhelming resource to navigate at this scale.