# SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

@inproceedings{Schtt2017SchNetAC, title={SchNet: A continuous-filter convolutional neural network for modeling quantum interactions}, author={Kristof Sch{\"u}tt and Pieter-Jan Kindermans and Huziel Enoc Sauceda Felix and Stefan Chmiela and Alexandre Tkatchenko and Klaus-Robert M{\"u}ller}, booktitle={NIPS}, year={2017} }

Deep learning has the potential to revolutionize quantum chemistry as it is ideally suited to learn representations for structured data and speed up the exploration of chemical space. While convolutional neural networks have proven to be the first choice for images, audio and video data, the atoms in molecules are not restricted to a grid. Instead, their precise locations contain essential physical information, that would get lost if discretized. Thus, we propose to use continuous-filter…

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