Learning continuous scattering length density profiles from neutron reflectivities using convolutional neural networks

Learning continuous scattering length density profiles from neutron reflectivities using convolutional neural networks

Interpreting neutron reflectivity (NR) data using ad hoc multi-layer models and physics-based models provides information about spatially resolved neutron scattering length density (NSLD) profiles. Recent improvements in data acquisition systems have allowed acquiring thousands of NR curves in a cou...

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Bibliographic Details
Main Authors: Brian Qu, Panagiotis Christakopoulos, Hanyu Wang, Jong Keum, Polyxeni P Angelopoulou, Peter V Bonnesen, Kunlun Hong, Mathieu Doucet, James F Browning, Miguel Fuentes-Cabrera, Rajeev Kumar
Format: Article
Language:English
Published: IOP Publishing 2024-01-01
Series:Machine Learning: Science and Technology
Subjects:
neutron reflectivity
polyzwitterions
convolutional neural networks
scattering length density
Online Access:https://doi.org/10.1088/2632-2153/ad9809
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https://doi.org/10.1088/2632-2153/ad9809

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