Towards the automated extraction of structural information from X-ray absorption spectra

X-ray absorption near-edge structure (XANES) spectroscopy is widely used across the natural sciences to obtain element specific atomic scale insight into the structure of matter. However, despite its increasing use owing to the proliferation of high-brilliance third- and fourth-generation light sources such as synchrotrons and X-ray free-electron lasers, decoding the wealth of information encoded within each spectra can sometimes be challenging and often requires detailed calculations. In this article we introduce a supervised machine learning method which aims at directly extracting structural information from a XANES spectrum. Using a convolutional neural network, trained using theoretical data, our approach performs this direct translation of spectral information and achieves a median error in first coordination shell bond-lengths of 0.1 angstrom, when applied to experimental spectra. By combining this with the bootstrap resampling approach, our network is also able to quantify the uncertainty expected, providing non-experts with a metric for the reliability of each prediction. This work sets the foundation for future work in delivering techniques that can accurately quantify structural information directly from XANES spectra. A machine learning model capable of extracting structural information from XANES spectra is introduced. This approach, analogous to a Fourier transform of EXAFS spectra, can predict first coordination shell bond-lengths with a median error of 0.1 angstrom.