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...

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Vydáno v:	Journal of applied crystallography Ročník 56; číslo 3; s. 868 - 883
Hlavní autoři:	Saurabh, Kumar, Dudenas, Peter J., Gann, Eliot, Reynolds, Veronica G., Mukherjee, Subhrangsu, Sunday, Daniel, Martin, Tyler B., Beaucage, Peter A., Chabinyc, Michael L., DeLongchamp, Dean M., Krishnamurthy, Adarsh, Ganapathysubramanian, Baskar
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	5 Abbey Square, Chester, Cheshire CH1 2HU, England International Union of Crystallography 01.06.2023 Blackwell Publishing Ltd
Témata:	Algorithms Biomaterials Biomedical materials Computer applications Computer Programs CyRSoXS Data collection Decision analysis Design parameters Exact solutions Graphics processing units Heterogeneity Inverse design Machine learning Modal data polarized resonant soft X‐ray scattering Polymers P‐RSoXS Robustness (mathematics) Scattering Simulation Spectroscopy Synchrotrons Tensors virtual instruments polarized resonant soft X-ray scattering virtual instruments P-RSoXS CyRSoXS
ISSN:	1600-5767, 0021-8898, 1600-5767
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Abstract	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 ed 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. This article presents CyRSoXS – an open‐source virtual instrument that uses GPUs to simulate polarized resonant soft X‐ray scattering (P‐RSoXS) patterns from real‐space material representations. It is significantly faster than the current state‐of‐the‐art software, and it enables quantitative extraction of orientation information from P‐RSoXS data. This enables a wide range of applications, including pattern fitting, co‐simulation, data exploration, machine learning workflows and multi‐modal data assimilation approaches.
AbstractList	This article presents CyRSoXS – an open-source virtual instrument that uses GPUs to simulate polarized resonant soft X-ray scattering (P-RSoXS) patterns from real-space material representations. It is significantly faster than the current state-of-the-art software, and it enables quantitative extraction of orientation information from P-RSoXS data. This enables a wide range of applications, including pattern fitting, co-simulation, data exploration, machine learning workflows and multi-modal data assimilation approaches. 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. 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 ed 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. This article presents CyRSoXS – an open‐source virtual instrument that uses GPUs to simulate polarized resonant soft X‐ray scattering (P‐RSoXS) patterns from real‐space material representations. It is significantly faster than the current state‐of‐the‐art software, and it enables quantitative extraction of orientation information from P‐RSoXS data. This enables a wide range of applications, including pattern fitting, co‐simulation, data exploration, machine learning workflows and multi‐modal data assimilation approaches. 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. 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 (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 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 to Python using . 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. 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. 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.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.
Author	Mukherjee, Subhrangsu Chabinyc, Michael L. Ganapathysubramanian, Baskar Sunday, Daniel Dudenas, Peter J. DeLongchamp, Dean M. Beaucage, Peter A. Krishnamurthy, Adarsh Reynolds, Veronica G. Saurabh, Kumar Gann, Eliot Martin, Tyler B.
Author_xml	– sequence: 1 givenname: Kumar surname: Saurabh fullname: Saurabh, Kumar organization: Iowa State University – sequence: 2 givenname: Peter J. surname: Dudenas fullname: Dudenas, Peter J. organization: National Institute of Standards and Technology – sequence: 3 givenname: Eliot surname: Gann fullname: Gann, Eliot organization: National Institute of Standards and Technology – sequence: 4 givenname: Veronica G. surname: Reynolds fullname: Reynolds, Veronica G. organization: University of California – sequence: 5 givenname: Subhrangsu surname: Mukherjee fullname: Mukherjee, Subhrangsu organization: National Institute of Standards and Technology – sequence: 6 givenname: Daniel surname: Sunday fullname: Sunday, Daniel organization: National Institute of Standards and Technology – sequence: 7 givenname: Tyler B. surname: Martin fullname: Martin, Tyler B. organization: National Institute of Standards and Technology – sequence: 8 givenname: Peter A. surname: Beaucage fullname: Beaucage, Peter A. organization: National Institute of Standards and Technology – sequence: 9 givenname: Michael L. surname: Chabinyc fullname: Chabinyc, Michael L. organization: University of California – sequence: 10 givenname: Dean M. surname: DeLongchamp fullname: DeLongchamp, Dean M. email: dean.delongchamp@nist.gov organization: National Institute of Standards and Technology – sequence: 11 givenname: Adarsh surname: Krishnamurthy fullname: Krishnamurthy, Adarsh organization: Iowa State University – sequence: 12 givenname: Baskar surname: Ganapathysubramanian fullname: Ganapathysubramanian, Baskar email: baskarg@iastate.edu organization: Iowa State University
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Keywords	polarized resonant soft X-ray scattering virtual instruments P-RSoXS CyRSoXS
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Snippet	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... 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... This article presents CyRSoXS – an open-source virtual instrument that uses GPUs to simulate polarized resonant soft X-ray scattering (P-RSoXS) patterns from...
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StartPage	868
SubjectTerms	Algorithms Biomaterials Biomedical materials Computer applications Computer Programs CyRSoXS Data collection Decision analysis Design parameters Exact solutions Graphics processing units Heterogeneity Inverse design Machine learning Modal data polarized resonant soft X‐ray scattering Polymers P‐RSoXS Robustness (mathematics) Scattering Simulation Spectroscopy Synchrotrons Tensors virtual instruments
Title	CyRSoXS: a GPU‐accelerated virtual instrument for polarized resonant soft X‐ray scattering
URI	https://onlinelibrary.wiley.com/doi/abs/10.1107%2FS1600576723002790 https://www.ncbi.nlm.nih.gov/pubmed/37284258 https://www.proquest.com/docview/2822273129 https://www.proquest.com/docview/2823499012 https://pubmed.ncbi.nlm.nih.gov/PMC10241048
Volume	56
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