Data as a Key Resource in Catalysis: A Community Account

Abstract 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.

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Publication Details

Journal
ChemCatChem
Published
2025-12-01
DOI
https://doi.org/10.1002/cctc.202501226
Citations
3
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
1.55
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article

Data as a Key Resource in Catalysis: A Community Account

Annette Trunschke, Pedro S.F. Mendes, Sandip De, Ramazan Yıldırım et al.
3 citations
ChemCatChem
Machine Learning in Materials Science
1.55
article

Data as a Key Resource in Catalysis: A Community Account

Annette Trunschke, Pedro S.F. Mendes, Sandip De, Ramazan Yıldırım, Núria López, R Fushimi
article en
3 citations

Abstract

Abstract 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.

ChemCatChemVol. 17(24)
University of Lisbon (PT), Idaho National Laboratory (US), Fritz Haber Institute of the Max Planck Society (DE), Institut Català d'Investigació Química (ES), Boğaziçi University (TR), Robert Bosch (Germany) (DE)
Openalex Percentile: Top 15%
Machine Learning in Materials Science
1.55
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