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Kumar Saurabh¹, Peter J Dudenas², Eliot Gann²

¹Department of Mechanical Engineering, Iowa State University, Ames, IA 50010, USA.

Journal of Applied Crystallography

|June 7, 2023

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A new computational tool, CyRSoXS, accelerates simulations for polarized resonant soft X-ray scattering (P-RSoXS) by over 1000x. This enables advanced analysis of soft materials, improving understanding of molecular orientation and heterogeneity.

Area of Science:

Soft Matter Physics
Materials Science
Spectroscopy

Background:

Polarized resonant soft X-ray scattering (P-RSoXS) is a powerful synchrotron technique for analyzing soft materials.
Extracting quantitative molecular orientation data from P-RSoXS patterns is computationally challenging due to complex sample properties.

Purpose of the Study:

To develop a high-performance computational framework for simulating P-RSoXS patterns.
To overcome the limitations of current simulation software for analyzing nanoscale material properties.

Main Methods:

Developed CyRSoXS, an open-source virtual instrument utilizing graphical processing units (GPUs) for rapid P-RSoXS simulations.
Implemented algorithms to optimize GPU performance by minimizing communication and memory usage.

Keywords:

CyRSoXS P-RSoXS polarized resonant soft X-ray scattering virtual instruments

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Validated the framework against analytical solutions and numerical comparisons.

Main Results:

Achieved a simulation speedup of over three orders of magnitude compared to existing software.
Demonstrated the accuracy and robustness of the CyRSoXS framework.
Enabled computationally intensive applications previously deemed unfeasible.

Conclusions:

CyRSoXS significantly accelerates P-RSoXS data analysis, making advanced applications accessible.
The Python integration democratizes usage and facilitates integration with machine learning and multi-modal data assimilation.
This advancement will drive new discoveries in soft materials research.