Repository for machine learning-assisted analysis of Polarized Neutron Reflectometry (PNR) measurements.

Setup

• Clone the repository:

git clone https://github.com/ninarina12/ML_PNR.git.

• Create a virtual environment with necessary dependencies:

conda create -n ml_pnr python=3.7.5

conda install --file requirements.txt

Workflow

• Generate high-volume synthetic data using the GenX simulation program [1] by creating a sim_${SAMPLE}.py file with the appropriate sample parameters, where ${SAMPLE} is replaced by the sample name (see examples). This script was typically executed by running:

mpiexec -n num_process python sim_${SAMPLE}.py

with num_process being the number of processes on which to run parallel simulations. Example parameters are based on nominal values of the target system.

• Visualize properties of the synthetic data within the pnr_properties.ipynb notebook.
• Train and evaluate a machine learning model within the pnr_vae.ipynb notebook.

Supporting code

• utils contains various utilities to assist with data import, processing, and plotting, as well as network components and architectures.

References

[1] Björck, Matts, and Gabriella Andersson. "GenX: an extensible X-ray reflectivity refinement program utilizing differential evolution." Journal of Applied Crystallography 40.6 (2007): 1174-1178.