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CyRSoXS: A GPU-accelerated virtual instrument for Polarized Resonant Soft X-ray Scattering (P-RSoXS)

Metadata Updated: March 18, 2023

Polarized Resonant Soft X-ray scattering (P-RSoXS) has emerged as a powerful synchrotron-based tool to measure structure in complex, chemically heterogeneous systems. P-RSoXS combines principles of X-ray scattering and X-ray spectroscopy; this combination provides unique sensitivity to molecular orientation and chemical heterogeneity in soft materials such as polymers and biomaterials. Quantitative extraction of orientation information from the P-RSoXS pattern data is challenging because the scattering processes depend on properties that are represented as three-dimensional tensors with heterogeneities at nanometer to sub-nanometer length scales. We overcome this challenge 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. Our computational framework -- called CyRSoXS -- is designed to maximize GPU performance, including algorithms that minimize both communication and memory footprints. We demonstrate the accuracy and robustness of our approach by validating against an extensive set of test cases, which include both analytical solutions and numerical comparisons, demonstrating a speedup of over three orders to the current state-of-the-art P-RSoXS simulation software. Such fast simulations open up a variety of applications that were previously computationally infeasible, including (a) pattern fitting, (b) co-simulation with the physical instrument for operando analytics, data exploration, and decision support, (c) data creation and integration into machine learning workflows, and (d) utilization in multi-modal data assimilation approaches. Finally, we abstract away the complexity of the computational framework from the end-user by exposing CyRSoXS to Python using Pybind. This democratizes usage by enabling seamless integration with various Python libraries, and also eliminates I/O requirements for large-scale parameter exploration and inverse design.

Access & Use Information

Public: This dataset is intended for public access and use. License: See this page for license information.

Downloads & Resources

Dates

Metadata Created Date March 18, 2023
Metadata Updated Date March 18, 2023
Data Update Frequency irregular

Metadata Source

Harvested from NIST

Additional Metadata

Resource Type Dataset
Metadata Created Date March 18, 2023
Metadata Updated Date March 18, 2023
Publisher National Institute of Standards and Technology
Maintainer
Identifier ark:/88434/mds2-2788
Data First Published 2023-03-07
Language en
Data Last Modified 2022-09-19 00:00:00
Category Physics:Condensed matter, Mathematics and Statistics:Numerical methods and software, Materials:Modeling and computational material science, Materials:Materials characterization, Materials:Polymers, Materials:Composites, Chemistry:Molecular characterization, Electronics:Organic electronics
Public Access Level public
Data Update Frequency irregular
Bureau Code 006:55
Metadata Context https://project-open-data.cio.gov/v1.1/schema/data.json
Schema Version https://project-open-data.cio.gov/v1.1/schema
Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
Harvest Object Id 79dca9ea-4a16-4e70-86c4-2d1768d7178f
Harvest Source Id 74e175d9-66b3-4323-ac98-e2a90eeb93c0
Harvest Source Title NIST
Homepage URL https://data.nist.gov/od/id/mds2-2788
License https://www.nist.gov/open/license
Program Code 006:045
Source Datajson Identifier True
Source Hash 74023b12c012ccf6eef44bedad06b423b405b777
Source Schema Version 1.1

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