Links and citations updated after npj Publication

README.md

@@ -8,9 +8,12 @@ autoXRD is a python package for automatic XRD pattern classification of thin-fil
 
 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:
 
-"**Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural network, (2019), Felipe Oviedo, Zekun Ren, et. al.  Link: [arXiv:1811.08425v](https://arxiv.org/abs/1811.08425v2)**
+"**Oviedo, F., Ren, Z., Sun, S., Settens, C., Liu, Z., Hartono, N. T. P., ... & Kusne, A. G. (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)
 
-**Accepted to NeurIPS 2018 ML for Molecules and Materials. Waiting for press in npj Computational Materials**
+
+"**Fast and interpretable 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)**
+
+**Accepted to NeurIPS 2018 ML for Molecules and Materials. Final version published npj Computational Materials 2019**
 
 
 ## Installation
@@ -21,15 +24,15 @@ To install, just clone the following repository:
 
 ## Usage
 
-Just run `space_group_a_CNN.py` , with the given datasets. 
+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:
 
 | Module | Description |
 | ------------- | ------------------------------ |
 | `space_group_a_CNN.py`      | Script for XRD space-group classification with a-CNN      |
 | `autoXRD`      | Module dedicated to XRD pattern preprocessing and data augmentation       |
 | `autoXRD_vis`   | Visualizer module for class activation maps (CAMs)     |
-| `Old-Demo / XRD_demo.ipynb` | Notebook containing a demo for physics-informed data augmentation, as presented in the **Accelerated Materials Development (AMD) Workshop** in Singapore. |
+| `Old-Demo / XRD_demo.ipynb` | Notebook containing a demo for physics-informed data augmentation, as presented in the **Accelerated Materials Development (AMD) Workshop** in Singapore. This is an outdated version without a-CNNs or CAM|
 
 
 ## Authors
@@ -45,3 +48,17 @@ Felipe Oviedo and "Danny" Zekun Ren
 ## Attribution
 
 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.
+
+### 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}
+  howpublished = {\url{https://doi.org/10.1038/s41524-019-0196-x}},
+}