ChemRxiv | Paper

CVAE

CVAE is a Conditional Variational Autoencoder which can predict the mono-metallic nanoparticle from a Pair Distribution Function.

1. CVAE
2. Getting started (own computer)
   1. Install requirements
   2. Simulate data
   3. Train model
   4. Predict
3. Author
4. Cite
5. Acknowledgments
6. License

Getting started (own computer)

Follow these step if you want to train the CVAE and predict with the model locally on your own computer.

Install requirements

See the install folder.

Simulate data

See the makeData folder.

Train model

To train your own CVAE model simply run:

python train.py

Predict

To predict a MMNP using CVAE or your own model on a PDF:

python predict.py

Authors

Andy S. Anker¹
Emil T. S. Kjær¹
Marcus N. Weng¹
Simon J. L. Billinge^{2, 3}
Raghavendra Selvan^{4, 5}
Kirsten M. Ø. Jensen¹

¹ Department of Chemistry and Nano-Science Center, University of Copenhagen, 2100 Copenhagen Ø, Denmark.
² Department of Applied Physics and Applied Mathematics Science, Columbia University, New York, NY 10027, USA.
³ Condensed Matter Physics and Materials Science Department, Brookhaven National Laboratory, Upton, NY 11973, USA.
⁴ Department of Computer Science, University of Copenhagen, 2100 Copenhagen Ø, Denmark.
⁵ Department of Neuroscience, University of Copenhagen, 2200, Copenhagen N.

Should there be any question, desired improvement or bugs please contact us on GitHub or through email: [email protected] or [email protected].

Cite

If you use our code or our results, please consider citing our papers. Thanks in advance!

@article{kjær2022DeepStruc,
title={DeepStruc: Towards structure solution from pair distribution function data using deep generative models},
author={Emil T. S. Kjær, Andy S. Anker, Marcus N. Weng, Simon J. L. Billinge, Raghavendra Selvan, Kirsten M. Ø. Jensen},
year={2022}}

@article{anker2020characterising,
title={Characterising the atomic structure of mono-metallic nanoparticles from x-ray scattering data using conditional generative models},
author={Anker, Andy Sode and Kjær, Emil TS and Dam, Erik B and Billinge, Simon JL and Jensen, Kirsten MØ and Selvan, Raghavendra},
year={2020}}

Acknowledgments

Our code is developed based on the the following publication:

@article{anker2020characterising,
title={Characterising the atomic structure of mono-metallic nanoparticles from x-ray scattering data using conditional generative models},
author={Anker, Andy Sode and Kjær, Emil TS and Dam, Erik B and Billinge, Simon JL and Jensen, Kirsten MØ and Selvan, Raghavendra},
year={2020}}

License

This project is licensed under the Apache License Version 2.0, January 2004 - see the LICENSE file for details.