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Carbon-based nanomaterials are a promising platform for diverse technologies, but their rational design requires a more detailed chemical control over their structure and properties than is currently available. A long-standing challenge for the field has been in the interpretation and use of experimental X-ray spectra, especially for the amorphous and disordered forms of carbon. Here, we outline a unified approach to simultaneously and quantitatively analyze experimental X-ray absorption spectroscopy (XAS) and X-ray photoelectron spectroscopy (XPS) spectra of carbonaceous materials. We employ unsupervised machine learning to identify the most representative chemical environments and deconvolute experimental data according to these spectral contributions. To fit experimental spectra we rely on ab initio references and use all the information available: to fit experimental XAS spectra, the whole XAS fingerprint (reference) spectra of certain sites are taken into account, rather than just peak positions, as ...

chemistry of materials

Understanding X-ray Spectroscopy of Carbonaceous Materials by Combining Experiments, Density Functional Theory, and Machine Learning. Part II: Quantitative Fitting of Spectra