AI assisted analysis of x-ray spectra

X-ray spectroscopy methods probe a synthesized material to provide detailed information about its structure at multiple levels of granularity. However, these methods are expensive and in some cases need to be performed at a synchrotron motivating rapid analysis of the data coming from these methods.

X-ray diffraction is critical to examine crystal structure of the material synthesized and also acts as a link between experiment and theory. In recent years, high-throughput measurement of x-ray diffraction has given us large volumes of data that are challenging to analyze. We show that by using physical constraints, matrix factorization, and introducing known knowledge we can rapidly map out quternary phase diagrams from experimental data.

X-ray absorption spectra are criticial to capture local chemical information of a material which are of great importance for functional properties such as catalysis, photocatalysis, battery electrodes etc. However, intepretation of these spectra is very time consuming and automated interpretation is likely to help maximize the use of expensive synchrotron resources. By combining random-forest and physically meaningful featurizations we show that we can automatically capture coordination number, bader charge, and nearest neighbor distances.