Sustainable global human existence requires a shift in the management and utilization of research data across all domains. In the realm of energy conversion materials, the intense exploration of halide perovskite materials since 2012 for potential use in optoelectronics has led to numerous startups actively pursuing commercialization efforts. However, the escalating volume of globally generated research data presents a critical challenge: the traditional dissemination strategies reliant on the publication of peer-reviewed articles. This causes big issues leading to the underutilization of information and research results generated. On the one hand, the spreading of information across numerous scientific journals results in an overwhelming, disparate dataset, impossible for any individual researcher to navigate. On the other hand, only a fraction of the experimental data gathered is being disseminated, with a bias toward positive outcomes, creating an incomplete and skewed dataset.

In response to this growing issue and “out of despair”, we initiated a collaborative data collection campaign in 2019 to consolidate research data from the sub-domain of perovskite solar cell test-cell development into a unified database [1]. While this dataset is currently one of the largest and most comprehensive in its field, it requires modernization to adhere to FAIR data principles and facilitate integration with machine learning tools, thereby making research more sustainable.

This presentation outlines our recent strides in expanding the Perovskite "literature" database on www.perovskitedatabase.com into an experimental database utilizing the NOMAD data infrastructure and management tools. Alongside the technical implementation, we are standardizing the data ontology for experimental samples and datasets, promoting easy replication and linkage to collect and utilize data generated by the global research community.

As a collaborative group of idealists, we dedicate our time and effort to this initiative, seeking to connect with like-minded researchers—especially those with advanced abilities and experience in research data management and AI. At this stage, input from AI experts is crucial to ensure the research data infrastructure we are building for experimental PV scientists fulfills the needs of the research community while enabling the efficient use of AI tools to make information exploitation faster and more sustainable.