Final readme updates

README.md

@@ -8,12 +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:
 
-"**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)
+"**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](https://doi.org/10.1038/s41524-019-0196-x)**
 
 
-"**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)**
+"**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)**
 
-**Accepted to NeurIPS 2018 ML for Molecules and Materials. Final version published npj Computational Materials 2019**
+**Accepted to NeurIPS 2018 ML for Molecules and Materials Workshop. Final version published npj Computational Materials 2019**
 
 
 ## Installation
@@ -43,22 +43,21 @@ Felipe Oviedo and "Danny" Zekun Ren
 | ------------- | ------------------------------ |
 | **AUTHORS**      | Felipe Oviedo and "Danny" Ren Zekun     | 
 | **VERSION**      | 0.9 / April, 2019     | 
+| **EMAIL OF REPO OWNER**      | [email protected]  | 
 ||                    |
 
 ## 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}},
-}
+## 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}}