This is a re-implementation of the SchNet neural network architecture in JAX, haiku, and JAX-MD.
schnax is intended as a drop-in replacement for the original pytorch implementation, allowing the use of trained weights obtained with SchNetPack.
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[1] K.T. Schütt. P.-J. Kindermans, H. E. Sauceda, S. Chmiela, A. Tkatchenko, K.-R. Müller.
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions. Advances in Neural Information Processing Systems 30, pp. 992-1002 (2017) link -
[2] K.T. Schütt. P.-J. Kindermans, H. E. Sauceda, S. Chmiela, A. Tkatchenko, K.-R. Müller.
SchNet - a deep learning architecture for molecules and materials. The Journal of Chemical Physics 148(24), 241722 (2018) 10.1063/1.5019779 -
[3] K.T. Schütt, P. Kessel, M. Gastegger, K. Nicoli, A. Tkatchenko, K.-R. Müller. SchNetPack: A Deep Learning Toolbox For Atomistic Systems. J. Chem. Theory Comput. 10.1021/acs.jctc.8b00908 arXiv:1809.01072. (2018)
