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...

Ausführliche Beschreibung

Bibliografische Detailangaben

Hauptverfasser:	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:	Artigo
Sprache:	Inglês
Veröffentlicht:	IOP Publishing 2024-01-01
Schriftenreihe:	Machine Learning: Science and Technology
Schlagworte:	neutron reflectivity polyzwitterions convolutional neural networks scattering length density
Online-Zugang:	https://doi.org/10.1088/2632-2153/ad9809
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