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

Abstract

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 couple of hours, which has led to
a need for automated data analysis tools to interpret NR measurements in real-time.
Here, we present a machine learning analysis workflow that uses a series of models,
based on a Convolutional Neural Network (CNN), to learn the relation between the
NSLDs and the NRs, and subsequently produce continuous NSLD profiles directly
from NRs. The usefulness of our CNN-based models is demonstrated by constructing
NSLDs from NRs of several films containing homopolymer polyzwitterions (PZs) and
diblock copolymers mixed with different types of salts. Comparisons of the NSLDs
with those constructed using ad hoc multi-layer models reveal a very good agreement,
suggesting the potential of CNN-based models for real-time automated data analysis
of NRs.