update readme (#70)

README.md

@@ -2,16 +2,13 @@
 
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-TorchANI is a pytorch implementation of ANI.
-
-Paper:
-
-* Smith JS, Isayev O, Roitberg AE. ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost. Chemical science. 2017;8(4):3192-203.
+TorchANI is a pytorch implementation of ANI. It is currently under alpha release, which means, the API is not stable yet. If you find a bug of TorchANI, or have some feature request, feel free to open an issue on GitHub, or send us a pull request.
 
 # Install
 
 TorchANI requires the master branch of PyTorch, which means:
 
+* The pytorch installed by `pip install` or `conda install` would not work.
 * You need to compile install the latest pytorch, see [official instructions](https://github.com/pytorch/pytorch#from-source).
 * Some update to TorchANI might require the user to recompile install the latest PyTorch. Before submitting a bug report, make sure you are running the latest PyTorch.
 
@@ -21,7 +18,15 @@ After installing the correct PyTorch, all you need is clone the repository and d
 pip install .
 ```
 
-# Development
+# Paper
+
+The original ANI-1 paper is:
+
+* Smith JS, Isayev O, Roitberg AE. ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost. Chemical science. 2017;8(4):3192-203.
+
+We are planning a seperate paper for TorchANI, it will be available when we are ready for beta release of TorchANI.
+
+# Note to TorchANI developers
 
 Never commit to the master branch directly. If you need to change something, create a new branch, submit a PR on GitHub.