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.

Document Type

Article

Document version

Published version

Language

English

Subject

Química

Pages

12 p.

Publisher

Chemistry Europe

Grant Agreement Number

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

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Rights

Attribution-NonCommercial 4.0 International

Attribution-NonCommercial 4.0 International

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Papers [1291]