Sasformer

This repository contains the source code for the Sasformer model from Multi-task Scattering-Model Classification and Parameter Regression of Nanostructures from Small-Angle Scattering Data.

SAS-55M-20k Dataset

The SAS-55M-20k dataset can be downloaded from figshare.

Installation

After you have downloaded the SAS-55M-20k dataset, clone the repository and navigate to the sasformer parent directory.

git clone [email protected]:by256/sasformer.git
cd <path>/<to>/sasformer

Using conda, create and activate the virtual environment and install the package.

conda env create -f environment.yaml
conda activate sasformer
python -m pip install -e .

Training

To train SASformer using the hyperparameters specified in the paper, update data_dir in scripts/train_sasformer.sh with the path of the SAS-55M-20k dataset and run the following script:

bash scripts/train_sasformer.sh

Note: we used a batch size of 1843 for training, but it is likely that you will have to adjust the batch_size parameter in this script to suit your hardware. Additionally, if you do not possess a graphics processing unit, change the accelerator parameter to cpu.

Inference

To see an example of inference on I(q) generated using sasmodels, see the sasformer/example.ipynb notebook.

You can open this file by installing Jupyter Notebook, running it in the sasformer/sasformer directory and clicking on the example.ipynb file in the jupyter notebook file browser:

python -m pip install notebook
cd sasformer
jupyter notebook
# click on `example.py`.

Results

To generate the test-set results presented in the paper, update data_dir in scripts/results.sh with the path of the SAS-55M-20k dataset and run the following script:

bash scripts/results.sh

You may have to adjust the batch_size and accelerator script parameters as mentioned in the Training section.

Citing

If you use the methods outlined in this repository, please cite the following work:

@article{Yildirim2024,
    author={Yildirim, Batuhan
    and Doutch, James
    and Cole, Jacqueline},
    title={Multi-Task Scattering-Model Classification and Parameter Regression of Nanostructures from Small-Angle Scattering Data},
    journal={Digital Discovery},
    year={2024},
    publisher={RSC},
    doi={10.1039/D3DD00225J},
    url={https://doi.org/10.1039/D3DD00225J}
}

Funding

This project was financially supported by the Science and Technology Facilities Council (STFC) and the Royal Academy of Engineering (RCSRF1819\7\10).

Acknowledgement

This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357.

This work benefited from the use of the SasView application, originally developed under NSF award DMR-0520547. SasView contains code developed with funding from the European Union’s Horizon 2020 research and innovation programme under the SINE2020 project, grant agreement No 654000.

License