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

TL;DR

CyRSoXS is a GPU-accelerated open-source tool that efficiently simulates polarized resonant soft X-ray scattering patterns, enabling advanced analysis and integration with machine learning for soft material research.

Contribution

We developed CyRSoXS, a GPU-optimized virtual instrument that significantly accelerates P-RSoXS simulations, validated its accuracy, and integrated it with Python to facilitate broad scientific applications.

Findings

Achieved over 1000x speedup compared to existing software.

Validated accuracy against analytical and numerical test cases.

Enabled new applications like pattern fitting and machine learning integration.

Abstract

Polarized Resonant Soft X-ray scattering (P-RSoXS) has emerged as a powerful synchrotron-based tool that combines 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 because the scattering processes originate from sample properties that must be represented as energy-dependent 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 GPUs to simulate P-RSoXS patterns from real-space material representations with nanoscale resolution. Our computational framework CyRSoXS (https://github.com/usnistgov/cyrsoxs) is designed to maximize GPU performance. 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 relative to the current state-of-the-art 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 eliminates I/O 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).