autoXRD 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.

autoXRD 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:

"Oviedo, F., Ren, Z., Sun, S., Settens, C., Liu, Z., Hartono, N. T. P., ... & 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

"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

Accepted to NeurIPS 2018 ML for Molecules and Materials Workshop. Final version published npj Computational Materials 2019

To install, just clone the following repository:

$ git clone https://github.com/PV-Lab/autoXRD.git

Just 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. The package contains the following module and scripts:

Felipe Oviedo and "Danny" Zekun Ren

This 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.

@article{oviedo2019fast, 
title={Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks},
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},
journal={npj Computational Materials},
volume={5},
number={1},
pages={60},
year={2019},
publisher={Nature Publishing Group}}