Unsupervised machine learning for accelerating discoveries from temperature dependent X-ray data

Data analysis is becoming an increasingly prominent bottleneck for many experimental fronts of quantum matter research. In particular, advancements in detector capabilities for X-ray and neutron scattering have enabled researchers to rapidly collect hundreds of GB of data. Here, we present a novel unsupervised machine learning approach for accelerating the analysis of temperature dependent single crystal X-ray diffraction data. Our method employs a mixture model to cluster over the temperature dependence of scattering intensities and readily identify phase transitions. It is capable of analyzing hundreds of GBs of data in the span of minutes, offering the tantalizing possibility of real time analysis. Applications to several materials are discussed.