Classifying and analyzing small‐angle scattering data using weighted k nearest neighbors machine learning techniques. Issue 2 (18th February 2020)

• Record Type: Journal Article
• Authors: Archibald, Richard K.; Doucet, Mathieu; Johnston, Travis; Young, Steven R.; Yang, Erika; Heller, William T.
• Is Part Of: Journal of applied crystallography. Volume 53:Issue 2(2020)
• Page Start: 326
• Page End: 334
• Publication Date: 2020-02-18
• Subjects: small‐angle scattering data -- machine learning -- modeling -- SasView; Crystallography -- Periodicals; 548.05
• DOI: 10.1107/S1600576720000552
• Languages: English
• ISSNs: 0021-8898

Abstract

Abstract : It is demonstrated how k nearest neighbor machine learning methods can be used to classify small‐angle scattering data for the most appropriate model to use for data analysis. The results show the promise of machine learning for helping small‐angle scattering practitioners translate measured data into scientific results. Abstract : A consistent challenge for both new and expert practitioners of small‐angle scattering (SAS) lies in determining how to analyze the data, given the limited information content of said data and the large number of models that can be employed. Machine learning (ML) methods are powerful tools for classifying data that have found diverse applications in many fields of science. Here, ML methods are applied to the problem of classifying SAS data for the most appropriate model to use for data analysis. The approach employed is built around the method of weighted k nearest neighbors (wKNN), and utilizes a subset of the models implemented in the SasView package (https://www.sasview.org/ ) for generating a well defined set of training and testing data. The prediction rate of the wKNN method implemented here using a subset of SasView models is reasonably good for many of the models, but has difficulty with others, notably those based on spherical structures. A novel expansion of the wKNN method was also developed, which uses Gaussian processes to produce local surrogate models for the classification, and this significantly improves the classification accuracy. Further, by integrating a stochastic gradient descent method during post‐processing, it is possible to leverage the local surrogate model both to classify the SAS data with high accuracy and to predict the structural parameters that best describe the data. The linking of data classification and model fitting has the potential to facilitate the translation of measured data into results for both novice and expert practitioners of SAS.