Bridging the Gap between the X-ray Absorption Spectroscopy and the Computational Catalysis Communities in Heterogeneous Catalysis: A Perspective on the Current and Future Research Directions

14 September 2022, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


X-ray absorption spectroscopy (XAS), (Extended X-ray Absorption Fine Structure (EXAFS) and X-ray Absorption Near-Edge Structure (XANES)), is a key technique within the heterogeneous catalysis community to probe the structure and properties of active site(s) for a diverse range of catalytic materials. However, the interpretation of the raw experimental data to derive an atomistic picture of the catalyst requires modeling and analysis; the EXAFS data are compared to a model and a goodness of fit parameter is used to judge the best fit. This EXAFS modeling can often be non-trivial and time-consuming; overcoming or improving these limitations remains a central challenge for the community. Considering these limitations, this Perspective highlights how recent developments in analysis software, increased availability of reliable computational models and application of data science tools can be used to improve the speed, accuracy, and reliability of EXAFS interpretation. In particular, we emphasize the advantages of combining theory and EXAFS as a unified technique that should be treated as a standard (when applicable) to identify catalytic sites and not two separate complementary methods. Building on the recent trends in the computational catalysis community, we also present a community-driven approach to adopt FAIR Guiding Principles for the collection, analysis, dissemination, and storage of XAS data. Written with both the experimental and theory audience in mind, we provide unified roadmap to foster collaborations between the two communities.


X-ray absorption spectroscopy
heterogeneous catalysis
machine learning
FAIR Guiding Principles
density functional theory


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