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README.md

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 # Materials Graph Library <img src="https://github.com/materialsvirtuallab/matgl/blob/main/assets/MatGL.png?raw=true" alt="matgl" width="30%" style="float: right">
 
@@ -50,7 +51,6 @@ in future.
 <p>Figure: Schematic of M3GNet/MEGNet</p>
 </div>
 
-
 ### MEGNet
 
 [MatErials Graph Network (MEGNet)][megnet] is an implementation of DeepMind's [graph networks][graphnetwork] for
@@ -178,24 +178,24 @@ information. If you are using any of the pretrained models, please cite the rele
 
 > **MEGNet**
 >
-> Chen, C.; Ye, W.; Zuo, Y.; Zheng, C.; Ong, S. P. _Graph Networks as a Universal Machine Learning Framework for
-> Molecules and Crystals._ Chem. Mater. 2019, 31 (9), 3564–3572. DOI: [10.1021/acs.chemmater.9b01294][megnet].
+> Chen, C.; Ye, W.; Zuo, Y.; Zheng, C.; Ong, S. P. *Graph Networks as a Universal Machine Learning Framework for
+> Molecules and Crystals.* Chem. Mater. 2019, 31 (9), 3564–3572. DOI: [10.1021/acs.chemmater.9b01294][megnet].
 
 > **Multi-fidelity MEGNet**
 >
-> Chen, C.; Zuo, Y.; Ye, W.; Li, X.; Ong, S. P. _Learning Properties of Ordered and Disordered Materials from
-> Multi-Fidelity Data._ Nature Computational Science, 2021, 1, 46–53. DOI: [10.1038/s43588-020-00002-x][mfimegnet].
+> Chen, C.; Zuo, Y.; Ye, W.; Li, X.; Ong, S. P. *Learning Properties of Ordered and Disordered Materials from
+> Multi-Fidelity Data.* Nature Computational Science, 2021, 1, 46–53. DOI: [10.1038/s43588-020-00002-x][mfimegnet].
 
 > **M3GNet**
 >
-> Chen, C., Ong, S.P. _A universal graph deep learning interatomic potential for the periodic table._ Nature
+> Chen, C., Ong, S.P. *A universal graph deep learning interatomic potential for the periodic table.* Nature
 > Computational Science, 2023, 2, 718–728. DOI: [10.1038/s43588-022-00349-3][m3gnet].
 
 ## FAQs
 
 1. **The `M3GNet-MP-2021.2.8-PES` differs from the original TensorFlow (TF) implementation!**
 
-   _Answer:_ `M3GNet-MP-2021.2.8-PES` is a refitted model with some data improvements and minor architectural changes.
+   *Answer:* `M3GNet-MP-2021.2.8-PES` is a refitted model with some data improvements and minor architectural changes.
    Porting over the weights from the TF version to DGL/PyTorch is non-trivial. We have performed reasonable benchmarking
    to ensure that the new implementation reproduces the broad error characteristics of the original TF implementation
    (see [examples][jupyternb]). However, it is not expected to reproduce the TF version exactly. This refitted model
@@ -204,22 +204,22 @@ information. If you are using any of the pretrained models, please cite the rele
 
 2. **I am getting errors with `matgl.load_model()`!**
 
-   _Answer:_ The most likely reason is that you have a cached older version of the model. We often refactor models to
+   *Answer:* The most likely reason is that you have a cached older version of the model. We often refactor models to
    ensure the best implementation. This can usually be solved by updating your `matgl` to the latest version
    and clearing your cache using the following command `mgl clear`. On the next run, the latest model will be
    downloaded. With effect from v0.5.2, we have implemented a model versioning scheme that will detect code vs model
    version conflicts and alert the user of such problems.
 
 3. **What pre-trained models should I be using?**
 
-   _Answer:_ There is no one definitive answer. In general, the newer the architecture and dataset, the more likely
+   *Answer:* There is no one definitive answer. In general, the newer the architecture and dataset, the more likely
    the model performs better. However, it should also be noted that a model operating on a more diverse dataset may
    compromise on  performance on a specific system. The best way is to look at the READMEs included with each model
    and do some tests on the systems you are interested in.
 
 4. **How do I contribute to matgl?**
 
-   _Answer:_ For code contributions, please fork and submit pull requests. You should read the
+   *Answer:* For code contributions, please fork and submit pull requests. You should read the
    [developer guide](developer.md) to understand the general design guidelines. We welcome pre-trained model
    contributions as well, which should also be submitted via PRs. Please follow the folder structure of the
    pretrained models. In particular, we expect all models to come with a `README.md` and notebook
@@ -229,7 +229,7 @@ information. If you are using any of the pretrained models, please cite the rele
 
 5. **None of your models do what I need. Where can I get help?**
 
-   _Answer:_ Please contact [Prof Ong][ongemail] with a brief description of your needs. For simple problems, we are
+   *Answer:* Please contact [Prof Ong][ongemail] with a brief description of your needs. For simple problems, we are
    glad to advise and point you in the right direction. For more complicated problems, we are always open to
    academic collaborations or projects. We also offer [consulting services][mqm] for companies with unique needs,
    including but not limited to custom data generation, model development and materials design.