CyRSoXS : a GPU-accelerated virtual instrument for polarized resonant soft X-ray scattering.

Autor: Saurabh K; Department of Mechanical Engineering, Iowa State University, Ames, IA 50010, USA., Dudenas PJ; Material Measurement Laboratory, National Institute of Standards and Technology (NIST), Gaithersburg, MD 20899, USA., Gann E; Material Measurement Laboratory, National Institute of Standards and Technology (NIST), Gaithersburg, MD 20899, USA., Reynolds VG; Materials Department, University of California, Santa Barbara, CA 93106, USA., Mukherjee S; Material Measurement Laboratory, National Institute of Standards and Technology (NIST), Gaithersburg, MD 20899, USA., Sunday D; Material Measurement Laboratory, National Institute of Standards and Technology (NIST), Gaithersburg, MD 20899, USA., Martin TB; Material Measurement Laboratory, National Institute of Standards and Technology (NIST), Gaithersburg, MD 20899, USA., Beaucage PA; NIST Center for Neutron Research, National Institute of Standards and Technology (NIST), Gaithersburg, MD 20899, USA., Chabinyc ML; Materials Department, University of California, Santa Barbara, CA 93106, USA., DeLongchamp DM; Material Measurement Laboratory, National Institute of Standards and Technology (NIST), Gaithersburg, MD 20899, USA., Krishnamurthy A; Department of Mechanical Engineering, Iowa State University, Ames, IA 50010, USA., Ganapathysubramanian B; Department of Mechanical Engineering, Iowa State University, Ames, IA 50010, USA.
Jazyk: angličtina
Zdroj: Journal of applied crystallography [J Appl Crystallogr] 2023 May 23; Vol. 56 (Pt 3), pp. 868-883. Date of Electronic Publication: 2023 May 23 (Print Publication: 2023).
DOI: 10.1107/S1600576723002790
Abstrakt: 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 - called CyRSoXS (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 for operando analytics, 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 exposing CyRSoXS to Python using Pybind . 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.
(© Kumar Saurabh et al. 2023.)
Databáze: MEDLINE
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