reflectorch 1.5.1

A Pytorch-based package for the analysis of reflectometry data

Reflectorch

Reflectorch is a machine learning Python package for the analysis of X-ray and neutron reflectometry data, written by Vladimir Starostin & Valentin Munteanu at the University of Tübingen. It provides functionality for the fast simulation of reflectometry curves on the GPU, customizable setup of the physical parameterization model and neural network architecture via YAML configuration files, and prior-aware training of neural networks as described in our paper Neural network analysis of neutron and X-ray reflectivity data incorporating prior knowledge.

Installation

Reflectorch can be installed from via pip:

pip install reflectorch

Alternatively, one can clone the entire Github repository and install the package in editable mode:

git clone https://github.com/schreiber-lab/reflectorch.git
pip install -e .

For development purposes, the package can be installed together with the optional dependencies for building the distribution, testing and documentation:

git clone https://github.com/schreiber-lab/reflectorch.git
pip install -e .[tests,docs,build]

Users with Nvidia GPUs need to additionally install Pytorch with CUDA support corresponding to their hardware and operating system according to the instructions from the Pytorch website

Get started

The full documentation of the package, containing tutorials and the API reference, was built with Jupyter Book and Sphinx and it is hosted at the address: https://schreiber-lab.github.io/reflectorch/.

We provide an interactive Google Colab notebook for exploring the basic functionality of the package:

Pretrained models (network weights and their corresponding configuration files) are hosted on Hugging Face. Two repositories are currently available:

Repository	Description
valentinsingularity/reflectivity	Research repository containing a large variety of XRR and NR models.
reflectorch-ILL	Curated repository of selected, validated NR models.

Contributing

If you'd like to contribute to the package, please see our Contributing Guidelines for details.

Citation

If you find our work useful in your research, please cite as follows:

@Article{Munteanu2025, 
doi = {10.21105/joss.08169}, 
url = {https://doi.org/10.21105/joss.08169}, 
year = {2025}, 
publisher = {The Open Journal}, 
volume = {10}, 
number = {115}, 
pages = {8169}, 
author = {Munteanu, Valentin and Starostin, Vladimir and Hinderhofer, Alexander and Gerlach, Alexander and Lapkin, Dmitry and Schreiber, Frank}, 
title = {reflectorch: a deep learning package for X-ray and neutron reflectometry}, 
journal = {Journal of Open Source Software} }

@Article{Munteanu2024,
  author    = {Munteanu, Valentin and Starostin, Vladimir and Greco, Alessandro and Pithan, Linus and Gerlach, Alexander and Hinderhofer, Alexander and Kowarik, Stefan and Schreiber, Frank},
  journal   = {Journal of Applied Crystallography},
  title     = {Neural network analysis of neutron and X-ray reflectivity data incorporating prior knowledge},
  year      = {2024},
  issn      = {1600-5767},
  month     = mar,
  number    = {2},
  volume    = {57},
  doi       = {10.1107/s1600576724002115},
  publisher = {International Union of Crystallography (IUCr)},
}