Invited Paper AS+SS-TuA3
The Center for Advanced Methods for Energy Research Applications (CAMERA):Mathematical Methods for Data Science from Experimental Facilities

Tuesday, November 8, 2016, 3:00 pm, Room 101B

The Center for Advanced Methods for Energy Research Applications (CAMERA), jointly funded by the U.S. Department of Energy Offices of Advanced Scientific Research (ASCR) and Basic Energy Sciences (BES), focuses on mathematical models, algorithms, and codes thatanalyze, interpret, and understand the information contained within experimental data, particularly arising from light sources and nanoscale facilities. Initial focus areas include ptychography, tomography, grazing incidence small-angle scattering, image analysis and reconstruction methods, fluctuation scattering, single particle imaging, fast electronic structure methods, and automatic materials characterization and design. In this talk, we will describe the structure of CAMERA, and summarize some of the major projects. In particular, we will discuss work on: (1) Algorithms for real-time streaming ptychography. Ptychographical phase retrieval is a non-linear optimization problem, made tractable through exploiting redundancy inherent in obtaining diffraction patterns from overlapping regions of the sample. Here, we describe SHARP: our "Scalable Hetereogeneous Adaptive Real-time Ptychography" framework that enables high-throughput streaming analysis. (2) New algorithms for fluctuation scattering and single particle imaging: In single particle diffraction (SPD) imaging, a large number of X-ray diffraction images are collected from individual particles, which are delivered to an ultrabright X-ray beam at random and unknown orientations through either a liquid droplet or aerosol delivery system. Recently, a new mathematical and algorithmic procedure has been introduced, known as "Multi-tiered Iterative Phasing" (MTIP), which simultaneously determines the orientations, 3D intensity function, complex phases, and the underlying molecular structure together in a single iterative process. (3) Machine learning methods for classification and characterization of scattering patterns. Grazing Incidence Small Angle X-ray Scattering (GISAXS) is an important reciprocal-space imaging modality which provides statistical information about a sample in 3-D. GISAXS is widely used for studying thin films that play a vital role as building blocks for the next generation of renewable energy technology. One challenge in GISAXS imaging is to be able to accurately infer properties of the material such as the crystal lattice corresponding to the sample from a single 2-D diffraction/scatter patterns. We will discuss our work using machine learning algorithms and convolution neural net classifiers to automatically provide structural details about the sample by analyzing the measured GISAXS diffraction patterns.