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 the 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 nontrivial 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 a unified roadmap to foster collaborations
between the two communities.