  Small angle X-ray scattering (SAXS) is extensively used in materials science
as a way of examining nanostructures. The analysis of experimental SAXS data
involves mapping a rather simple data format to a vast amount of structural
models. Despite various scientific computing tools to assist the model
selection, the activity heavily relies on the SAXS analysts' experience, which
is recognized as an efficiency bottleneck by the community. To cope with this
decision-making problem, we develop and evaluate the open-source, Machine
Learning-based tool SCAN (SCattering Ai aNalysis) to provide recommendations on
model selection. SCAN exploits multiple machine learning algorithms and uses
models and a simulation tool implemented in the SasView package for generating
a well defined set of datasets. Our evaluation shows that SCAN delivers an
overall accuracy of 95%-97%. The XGBoost Classifier has been identified as the
most accurate method with a good balance between accuracy and training time.
With eleven predefined structural models for common nanostructures and an easy
draw-drop function to expand the number and types training models, SCAN can
accelerate the SAXS data analysis workflow.
