2025-12-09
The deployment of artificial intelligence (AI) is transforming the scientific fields central to interdisciplinary catalysis research. By enabling more effective use of data, AI (including simpler machine learning and data science tools) holds great promise for accelerating discoveries. However, progress has so far been modest, largely due to the lack of standardized, machine-readable, and openly shared catalysis data. This perspective, accounting for community insights emerging at conferences, analyses the underlying reasons for these challenges and proposes solutions to a future whereFAIR data management becomes an integral part of research in catalysis. In the short-term, we deem that mandatory FAIR data depositing prior to scientific publications along with consensualized top-down guidelines on data sharing powered by ease-to-use tools can make the necessary step change happen to catalyse data as key resource in our community.
Article
Versió publicada
Anglès
12 p.
Chemistry Europe
Fundação para a Ciência e a Tecnologia funding through CQE (UIDB/00100/2020; UIDP/00100/2020), IMS (LA/P/0056/2020), and the CausalCat project (2023.12566.PEX).
BMBF, FKZ 03HY203A (AmmoRef / TransHyde), FKZ 03EW0015B (CatLab), and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), in the framework of the project FAIRmat—FAIR Data Infrastructure for Condensed-Matter Physics and the Chemical Physics of Solids, project number 460197019
U.S. Department of Energy, Industrial Technologies Office, R&D Projects DE-FOA-0002252-1775 and DE-FOA-0002997-2008 under contract no. DE-AC07-05ID14517
Papers [1292]