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Energy","sub_category":"Photovoltaics and Solar Energy","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"\nautoXRD\n===========\n## Description\n\n\nautoXRD is a python package for automatic XRD pattern classification of thin-films, tweaked for small and class-imbalanced datasets. The main application of the package is high-throughput screening of novel materials.\n\nautoXRD performs physics-informed data augmentation to solve the small data problem, implements a state-of-the-art a-CNN architecture and allows interpretation using Average Class Activation Maps (CAMs), according to the following publications:\n\n\"**Oviedo, F., Ren, Z., Sun, S., Settens, C., Liu, Z., Hartono, N. T. P., ... \u0026 Buonassisi, T. (2019). Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks. npj Computational Materials, 5(1), 60.\"  Link: [https://doi.org/10.1038/s41524-019-0196-x](https://doi.org/10.1038/s41524-019-0196-x)**\n\n\n\"**Fast classification of small X-ray diffraction datasets using data augmentation and deep neural networks, (2019), Felipe Oviedo, Zekun Ren, et. al.  Link: [arXiv:1811.08425v](https://arxiv.org/abs/1811.08425v2)**\n\nAccepted to NeurIPS 2018 ML for Molecules and Materials Workshop. Final version published npj Computational Materials 2019\n\n\n## Installation\n\nTo install, just clone the following repository:\n\n`$ git clone https://github.com/PV-Lab/autoXRD.git`\n\n## Usage\n\nJust run `space_group_a_CNN.py` , with the given datasets. Note that this performs classification for patterns into 7 space-groups. Dimensionality data is not included in the code, please contact authors if interested.\nThe package contains the following module and scripts:\n\n| Module | Description |\n| ------------- | ------------------------------ |\n| `space_group_a_CNN.py`      | Script for XRD space-group classification with a-CNN      |\n| `autoXRD`      | Module dedicated to XRD pattern preprocessing and data augmentation       |\n| `autoXRD_vis`   | Visualizer module for class activation maps (CAMs)     |\n| `Demo / XRD_dimensionality_demo.ipynb` | Notebook containing a demo for physics-informed data augmentation. This is a version with a modified CNN and no CAM to speed up the computation\n\n\n## Authors\nFelipe Oviedo and \"Danny\" Zekun Ren\n\n\n||                    |\n| ------------- | ------------------------------ |\n| **AUTHORS**      | Felipe Oviedo and \"Danny\" Ren Zekun     | \n| **VERSION**      | 1.0 / May, 2019     | \n| **EMAIL OF REPO OWNER**      | foviedo@mit.edu  | \n||                    |\n\n## Attribution\n\nThis work is under an Apache 2.0 License and data policies of Nature Partner Journal Computational Materials. Please, acknowledge use of this work with the apropiate citation.\n\n## Citation\n\n    @article{oviedo2019fast, \n    title={Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks},\n    author={Oviedo, Felipe and Ren, Zekun and Sun, Shijing and Settens, Charles and Liu, Zhe and Hartono, Noor Titan Putri and Ramasamy, Savitha and DeCost, Brian L and Tian, Siyu IP and Romano, Giuseppe and others},\n    journal={npj Computational Materials},\n    volume={5},\n    number={1},\n    pages={60},\n    year={2019},\n    publisher={Nature Publishing Group}}\n","funding_links":[],"readme_doi_urls":["https://doi.org/10.1038/s41524-019-0196-x"],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":[],"project_url":"https://ost.ecosyste.ms/api/v1/projects/19768","html_url":"https://ost.ecosyste.ms/projects/19768"}