In the past decade, advances in source brightness, instrumentation, and computing power have enabled the routine collection of large volumes of single crystal x-ray diffraction with high efficiency at synchrotron sources such as the Advanced Photon Source. The size of the data sets and the speed of their measurements present both formidable challenges and exciting opportunities. The challenges are to ensure that data reduction and analysis keep pace with data collection so that scientists can interpret the data, preferably during the experiment. The opportunities are that these new capabilities enable completely new modes of analysis that are impossible with more limited data sets.

AXMAS is a multi-disciplinary project to use advanced computational tools to address the challenges and realize the opportunities provided by this x-ray data. Project members, who are based at Argonne National Laboratory and Cornell University, comprise experimentalists, theorists, and computational scientists, forming a closely coupled team to generate tools that interrogate the data sets in novel ways, using unsupervised machine learning and multidimensional spectral analysis. These pages describe the experimental and computational methods used and describes the software developed, all of which is available in open-source repositories.

This material is based upon work supported by the U.S. Department of Energy,Office of Science, Office of Basic Energy Sciences, Materials Science and Engineering Division.