Machine Learning Science and Technology · 2021 · 24 citations · 12 references
Natural SciencesMaterials EngineeringMaterials ScienceMachine LearningMaterials CharacterizationThin FilmsDeep LearningNeutron ScatteringScattering AnalysisX-ray Data AnalysisAccuracyData Pipeline AutomationData Pipeline DesignData Pipeline ProcessingNeutron ReflectionNeutron ReflectometryNeutronics AnalysisX-ray Reflectivity
Abstract We describe a neural network-based tool for the automatic estimation of thin film thicknesses and scattering length densities from neutron reflectivity curves. The neural network sits within a data pipeline, that takes raw data from a neutron reflectometer, and outputs data and parameter estimates into a fitting program for end user analysis. Our tool deals with simple cases, predicting the number of layers and layer parameters up to three layers on a bulk substrate. This provides good accuracy in parameter estimation, while covering a large portion of the use case. By automating steps in data analysis that only require semi-expert knowledge, we lower the barrier to on-experiment data analysis, allowing better utility to be made from large scale facility experiments. Transfer learning showed that our tool works for x-ray reflectivity, and all code is freely available on GitHub (neutron-net 2020, available at: https://github.com/xmironov/neutron-net ) (Accessed: 25 June 2020).
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Surface Studies of Solids by Total Reflection of X-Rays
L. G. Parratt · Physical Review · 1954 · 5.1K citations