Machine Learning-Assisted Analysis of Small Angle X-ray Scattering

TL;DR

This paper introduces SCAN, an open-source machine learning tool that enhances the efficiency and accuracy of small angle X-ray scattering data analysis by providing model selection recommendations, thereby reducing reliance on expert experience.

Contribution

The paper presents SCAN, a novel machine learning-based tool that automates model selection in SAXS analysis, achieving high accuracy and user-friendly expansion capabilities.

Findings

SCAN achieves 95%-97% accuracy in model recommendation.

XGBoost is the most accurate algorithm with efficient training.

The tool accelerates SAXS data analysis workflow.

Abstract

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.