Neural network analysis of neutron and X‐ray reflectivity data: automated analysis using mlreflect, experimental errors and feature engineering. Issue 2 (2nd April 2022)

• Record Type: Journal Article
• Authors: Greco, Alessandro; Starostin, Vladimir; Edel, Evelyn; Munteanu, Valentin; Rußegger, Nadine; Dax, Ingrid; Shen, Chen; Bertram, Florian; Hinderhofer, Alexander; Gerlach, Alexander; Schreiber, Frank
• Is Part Of: Journal of applied crystallography. Volume 55:Issue 2(2022)
• Page Start: 362
• Page End: 369
• Publication Date: 2022-04-02
• Subjects: reflectometry -- data analysis -- machine learning -- Python; Crystallography -- Periodicals; 548.05
• DOI: 10.1107/S1600576722002230
• Languages: English
• ISSNs: 0021-8898

Abstract

Abstract : A Python‐based analysis pipeline for the fast analysis of X‐ray and neutron reflectivity data using neural networks is presented. Abstract : The Python package mlreflect is demonstrated, which implements an optimized pipeline for the automated analysis of reflectometry data using machine learning. The package combines several training and data treatment techniques discussed in previous publications. The predictions made by the neural network are accurate and robust enough to serve as good starting parameters for an optional subsequent least‐mean‐squares (LMS) fit of the data. For a large data set of 242 reflectivity curves of various thin films on silicon substrates, the pipeline reliably finds an LMS minimum very close to a fit produced by a human researcher with the application of physical knowledge and carefully chosen boundary conditions. The differences between simulated and experimental data and their implications for the training and performance of neural networks are discussed. The experimental test set is used to determine the optimal noise level during training. The extremely fast prediction times of the neural network are leveraged to compensate for systematic errors by sampling slight variations in the data.