Small angle neutron scattering in McStas: Optimization for high-throughput virtual experiments

In this work, we present the development of small-angle scattering components in McStas that describe the neutron interaction with 70 different form and structure factors. We describe the considerations taken into account for the generation of these components, such as the incorporation of polydispersity and orientational distribution effects in the Monte Carlo simulation. These models can be parallelized by means of multi-core simulations and graphical processing units. The acceleration schemes for the aforementioned models are benchmarked, and the resulting performance is presented. This allows the estimation of computation times in high-throughput virtual experiments. The presented work enables the generation of large datasets of virtual experiments that can be explored and used by machine learning algorithms.

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Publication Details

Journal
Journal of Neutron Research
Published
2024-11-01
DOI
https://doi.org/10.1177/10238166251313936
Citations
1
Primary Topic
Nuclear Physics and Applications
Type
article
Field-Weighted Citation Impact
0.61

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article

Small angle neutron scattering in McStas: Optimization for high-throughput virtual experiments

Klaus Lieutenant, José Ignacio Robledo, Peter Kjær Willendrup
1 citations
Journal of Neutron Research
Nuclear Physics and Applications
0.61
article

Small angle neutron scattering in McStas: Optimization for high-throughput virtual experiments

Klaus Lieutenant, José Ignacio Robledo, Peter Kjær Willendrup
article en
1 citations

Abstract

In this work, we present the development of small-angle scattering components in McStas that describe the neutron interaction with 70 different form and structure factors. We describe the considerations taken into account for the generation of these components, such as the incorporation of polydispersity and orientational distribution effects in the Monte Carlo simulation. These models can be parallelized by means of multi-core simulations and graphical processing units. The acceleration schemes for the aforementioned models are benchmarked, and the resulting performance is presented. This allows the estimation of computation times in high-throughput virtual experiments. The presented work enables the generation of large datasets of virtual experiments that can be explored and used by machine learning algorithms.

Journal of Neutron ResearchVol. 26(4)
Forschungszentrum Jülich (DE), Danish Geotechnical Society (DK), Technical University of Denmark (DK)
HORIZON EUROPE Marie Sklodowska-Curie Actions
Openalex Percentile: Top 27%
Nuclear Physics and Applications
0.61
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Small angle neutron scattering in McStas: Optimization for high-throughput virtual experiments — Klaus Lieutenant, José Ignacio Robledo, et al. · Journal of Neutron Research (2024) | TGRS Research Map | TGRS