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Update readme, add bibtex file.
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CITATION.bib

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@article{schopmans2023pxrd,
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title = {Neural networks trained on synthetically generated crystals can extract structural information from ICSD powder X-ray diffractograms},
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author = {Henrik Schopmans and Patrick Reiser and Pascal Friederich},
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year = {2023},
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eprint = {2303.11699},
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doi = {10.48550/arXiv.2303.11699},
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archiveprefix = {arXiv}
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}

README.md

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# ML for pXRDs using synthetic crystals
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This repository contains the code of the publication "Neural networks trained on
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This repository contains the code of the publication ["Neural networks trained on
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synthetically generated crystals can extract structural information from ICSD
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powder X-ray diffractograms". It can be used to train machine learning models
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powder X-ray diffractograms"](https://arxiv.org/abs/2303.11699). It can be used to train machine learning models
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(e.g., for the classification of space groups) on powder XRD diffractograms
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simulated on-the-fly from synthetically generated random crystal structures.
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You can find details about this project in our [`paper`](https://arxiv.org/abs/2303.11699). If you want to cite our work, you can use the provided bibtex file [CITATION.bib](CITATION.bib).
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If you have any problems using the provided software, if documentation is
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missing, or if you find any bugs, feel free to add a new issue on GitHub.
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- `accuracy gap`: `accuracy random - accuracy match`
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Additionally to those metrics, after each epoch, the current learning rate and the current
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size of the `ray` queue object (indicating if enough workers are used) are logged.
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# Citing
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To cite this repository, please refer to our publication:
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- TODO: Add reference to arXiv paper
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size of the `ray` queue object (indicating if enough workers are used) are logged.

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