Data-driven X-ray absorption spectral analysis at user facilities

X-ray absorption spectroscopy is a premier element-specific experimental technique for materials characterization. Specifically, X-ray absorption near edge structure (XANES) encodes rich local structural and chemical information around absorber sites, making it a powerful tool for probing physical and chemical processes at large synchrotron facilities. However, the correlation between XANES spectral features and the underlying local structural motifs or electronic descriptors is obscure. Recent progress in materials discovery using smart automation and in situ / operando experiments underscores emerging challenges and opportunities of spectral analysis in real time. This challenge cannot be tackled by empirical fingerprints, which have practical limitations on data availability and diversity, or first-principles simulations due to the high computational cost. Here I discuss the recent progress of the data-driven spectral analysis pipeline by combining first-principles theory, data analytics tools and software development to decipher the structure-spectrum relationship. I highlight the utility of key modules of this pipeline, including 1) Benchmark modules that quantify the effects of key approximations and implementations in different computational methods or codes; 2) Workflow modules that aim to lower the barrier for non-expert practitioners by providing appropriate calculation input parameters based on systematic benchmarks; 3) Database modules that provide FAIR spectral datasets; 4) Machine learning modules that accelerate spectral simulation and extract physical descriptors from spectra.