One of the main goals of neutron data analysis is to determine the internal structure of materials from their neutron scattering profiles. These structures are defined by a crystallographic class label and a set of real-valued parameters specific to that class. Existing structure analysis approaches use computationally expensive loop refinements methods that routinely take days, and even weeks, to complete. Additionally, the outcomes often rely on the fidelity of physical models that are computed during the refinement process. Here, we evaluate the feasibffity of using trained data-driven machine learning models as fast and accurate substitutes for these expensive methods. We report on the efficacies of a variety of ML models, including convolutional neural networks, auto-encoders, random forests and combinations thereof, in addition to techniques such as transfer learning in predicting these structural parameters. Specifically, we evaluate two categories of models which we call class-conditional and integrated. The first relies on a two-stage inference pipeline in which a crystallographic class label is first predicted followed by regression to predict the length/angle parameters. In the second category, the classification and regression tasks are performed as a single learning task. We train these models on synthetically generated data, validate them against experimental observa-tions and show that integrated models outperform their class-conditional counterparts opening up the possibffity of deep learning models as a viable alternative to existing resource-intensive loop refinement methods in neutron data analysis.
