Include TorchANI preprint (#465)

README.md

@@ -60,12 +60,9 @@ To run the tests and examples, you must manually download a data package
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 # Paper
+TorchANI [Preprint](https://chemrxiv.org/articles/TorchANI_A_Free_and_Open_Source_PyTorch_Based_Deep_Learning_Implementation_of_the_ANI_Neural_Network_Potentials/12218294)
 
-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.
+* Gao X, Ramezanghorbani F, Isayev O, Smith JS, Roitberg AE. TorchANI: A Free and Open Source PyTorch Based Deep Learning Implementation of the ANI Neural Network Potentials. [![DOI for Citing](https://img.shields.io/badge/DOI-10.26434%2Fchemrxiv.12218294.v1-green.svg)](https://doi.org/10.26434/chemrxiv.12218294.v1)
 
 See also: [isayev/ASE_ANI](https://github.com/isayev/ASE_ANI)