Navigating The Vast Materials Landscape for Photovoltaics with NOMAD

Accelerating the development of innovative solar materials demands a fundamental transformation in lab methods, particularly in enhancing efficiency in production, characterization, and device integration. Numerous research labs are beginning to adopt more efficient methods to achieve the necessary acceleration, including high throughput computation and experimentation, and the emergence of some autonomous labs. All these efforts generate a massive volume of data that needs to be organized, automated and made ready for AI analyses and programmatic access.<br/>This talk offers insight into how NOMAD (https://nomad-lab.eu), an open-source platform developed by the NFDI (Germany’s National Research Data Infrastructure) consortium FAIRmat (https://fairmat-nfdi.eu), can help individuals and labs in this task. NOMAD is designed to tackle these challenges by enhancing the accessibility and usefulness of materials science data following the FAIR principles - making data Findable, Accessible, Interoperable, and Reusable. I will overview the dynamic improvements within the NOMAD infrastructure beyond computational materials science, specifically tailored for experimental solar cell research, and highlight an application created for detailed visualization and investigation of a broad, AI-ready solar cell dataset. As an illustration, I will examine the historical advancement of charge transport layers for perovskite solar cells using network analysis, demonstrating how it is possible to navigate effectively this extensive materials space.<br/>To provide a digitization platform for experimental developments, the NOMAD platform incorporates a flexible electronic lab notebook (ELN), configured for customization to meet the specific needs of individual research laboratories, thereby facilitating the efficient capture, transfer, and processing of AI-ready data and metadata within a well-organized FAIR database framework.<br/>Ultimately, this presentation aims to illuminate the path forward in the realm of semiconductor materials for solar applications, guided by data-centric methodologies and collaborative infrastructures like NOMAD, which are indispensable in accelerating the journey toward efficient and sustainable solar energy solutions.