Machine learning (ML) offers exciting new opportunities to extract more information from scattering data. At neutron scattering user facilities, ML has the potential to help accelerate scientific productivity by empowering facility users with insight into their data which has traditionally been supplied by scattering experts. Such support can help in both speeding up common modeling problems for users, as well as help solve harder problems that are normally time consuming and difficult to address with standard methods. This article explores the recent ML work undertaken at Oak Ridge National Laboratory involving neutron scattering data. We cover materials structure modeling for diffuse scattering, powder diffraction, and small-angle scattering. We also discuss how ML can help to model the response of the instrument more precisely, as well as enable quick extraction of information from neutron data. The application of super-resolution techniques to small-angle scattering and peak extraction for diffraction will be discussed.

机器学习（ML）提供了令人兴奋的新机会，可以从分散的数据中提取更多信息。在中子散射用户设施中，机器学习有潜力通过使设施用户了解其数据（这些数据通常由散射专家提供）来帮助加快科学生产率。这样的支持既可以帮助加快用户的常见建模问题，又可以帮助解决通常比较耗时且难以使用标准方法解决的更棘手的问题。本文探讨了在橡树岭国家实验室进行的有关中子散射数据的最新ML工作。我们涵盖了用于散射，粉末衍射和小角度散射的材料结构建模。我们还将讨论ML如何帮助您更精确地对仪器的响应进行建模，以及能够从中子数据中快速提取信息。将讨论超分辨率技术在小角度散射和衍射峰提取中的应用。