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SPIND(sparse-pattern indexing) is an auto-indexing algorithm for sparse snapshot diffraction patterns (`stills') that requires the positions of only five Bragg peaks in a single pattern, when provided with unit-cell parameters. The capability ofSPINDis demonstrated for the orientation determination of sparse diffraction patterns using simulated data from microcrystals of a small inorganic molecule containing three iodines, 5-amino-2,4,6-triiodoisophthalic acid monohydrate (I3C) [Beck & Sheldrick (2008),Acta Cryst.E64, o1286], which is challenging for commonly used indexing algorithms.SPIND, integrated withCrystFEL[Whiteet al.(2012),J. Appl. Cryst.45, 335–341], is then shown to improve the indexing rate and quality of merged serial femtosecond crystallography data from two membrane proteins, the human δ-opioid receptor in complex with a bi-functional peptide ligand DIPP-NH2and the NTQ chloride-pumping rhodopsin (CIR). The study demonstrates the suitability ofSPINDfor indexing sparse inorganic crystal data with smaller unit cells, and for improving the quality of serial femtosecond protein crystallography data, significantly reducing the amount of sample and beam time required by making better use of limited data sets.SPINDis written in Python and is publicly available under the GNU General Public License from https://github.com/LiuLab-CSRC/SPIND.
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CyRSoXS : a GPU-accelerated virtual instrument for polarized resonant soft X-ray scattering
Polarized resonant soft X-ray scattering (P-RSoXS) has emerged as a powerful synchrotron-based tool that combines the principles of X-ray scattering and X-ray spectroscopy. P-RSoXS provides unique sensitivity to molecular orientation and chemical heterogeneity in soft materials such as polymers and biomaterials. Quantitative extraction of orientation information from P-RSoXS pattern data is challenging, however, because the scattering processes originate from sample properties that must be represented as energy-dependent three-dimensional tensors with heterogeneities at nanometre to sub-nanometre length scales. This challenge is overcome here by developing an open-source virtual instrument that uses graphical processing units (GPUs) to simulate P-RSoXS patterns from real-space material representations with nanoscale resolution. This computational framework – calledCyRSoXS(https://github.com/usnistgov/cyrsoxs) – is designed to maximize GPU performance, including algorithms that minimize both communication and memory footprints. The accuracy and robustness of the approach are demonstrated by validating against an extensive set of test cases, which include both analytical solutions and numerical comparisons, demonstrating an acceleration of over three orders of magnitude relative to the current state-of-the-art P-RSoXS simulation software. Such fast simulations open up a variety of applications that were previously computationally unfeasible, including pattern fitting, co-simulation with the physical instrument foroperandoanalytics, data exploration and decision support, data creation and integration into machine learning workflows, and utilization in multi-modal data assimilation approaches. Finally, the complexity of the computational framework is abstracted away from the end user by exposingCyRSoXSto Python usingPybind. This eliminates input/output requirements for large-scale parameter exploration and inverse design, and democratizes usage by enabling seamless integration with a Python ecosystem (https://github.com/usnistgov/nrss) that can include parametric morphology generation, simulation result reduction, comparison with experiment and data fitting approaches.
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- Award ID(s):
- 1808622
- PAR ID:
- 10479109
- Publisher / Repository:
- International Union of Crystallography
- Date Published:
- Journal Name:
- Journal of Applied Crystallography
- Volume:
- 56
- Issue:
- 3
- ISSN:
- 1600-5767
- Page Range / eLocation ID:
- 868 to 883
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1107/S1600576723002790
