期刊
JOURNAL OF APPLIED CRYSTALLOGRAPHY
卷 53, 期 -, 页码 326-334出版社
INT UNION CRYSTALLOGRAPHY
DOI: 10.1107/S1600576720000552
关键词
small-angle scattering data; machine learning; modeling; SasView
资金
- Laboratory Directed Research and Development Program of Oak Ridge National Labora-tory [LDRD-8235]
- NSF award [DMR-0520547]
- European Union's Horizon 2020 research and innovation programme under the SINE2020 project [654000]
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.
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