Gao, Xiang, Ramezanghorbani, Farhad, Isayev, Olexandr, Smith, Justin S., and Roitberg, Adrian E.. TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials. Retrieved from https://par.nsf.gov/biblio/10198963. Journal of Chemical Information and Modeling 60.7 Web. doi:10.1021/acs.jcim.0c00451.

Gao, Xiang, Ramezanghorbani, Farhad, Isayev, Olexandr, Smith, Justin S., & Roitberg, Adrian E.. TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials. Journal of Chemical Information and Modeling, 60 (7). Retrieved from https://par.nsf.gov/biblio/10198963. https://doi.org/10.1021/acs.jcim.0c00451

Gao, Xiang, Ramezanghorbani, Farhad, Isayev, Olexandr, Smith, Justin S., and Roitberg, Adrian E.. "TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials". Journal of Chemical Information and Modeling 60 (7). Country unknown/Code not available. https://doi.org/10.1021/acs.jcim.0c00451. https://par.nsf.gov/biblio/10198963.

@article{osti_10198963,
place = {Country unknown/Code not available}, title = {TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials}, url = {https://par.nsf.gov/biblio/10198963}, DOI = {10.1021/acs.jcim.0c00451}, abstractNote = {}, journal = {Journal of Chemical Information and Modeling}, volume = {60}, number = {7}, author = {Gao, Xiang and Ramezanghorbani, Farhad and Isayev, Olexandr and Smith, Justin S. and Roitberg, Adrian E.}, }