Unsupervised machine learning of single crystal x-ray diffraction data

Data analysis has become a critical bottleneck in reciprocal spaces studies of single crystal x-ray diffraction. This is because while dramatic leaps in detector technology have enabled the collection of large amounts of data in short amounts of time, a parallel development in data analysis has been lacking. Current approaches for investigating this data often require highly devoted researchers to manually comb through it looking for evidence of new physics. This is time-intensive and increasingly infeasible as datasets continue to grow in size. Here we discuss an unsupervised machine learning approach to studying phase transitions in single crystal x-ray diffraction data. Our method leverages novel techniques for approximate Bayesian inference to identify contributions to the scattering intensity with distinct physical origins. It is highly scalable and employs recent developments in numerical linear algebra for memory efficiency and high speed on parallel computing hardware, such as graphical processing units.