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Deep learning has the potential to revolutionize quantum chemistry as it isideally suited to learn representations for structured data and speed up theexploration of chemical space. While convolutional neural networks have provento be the first choice for images, audio and video data, the atoms in moleculesare not restricted to a grid. Instead, their precise locations containessential physical information, that would get lost if discretized. Thus, wepropose to use continuous-filter convolutional layers to be able to model localcorrelations without requiring the data to lie on a grid. We apply those layersin SchNet: a novel deep learning architecture modeling quantum interactions inmolecules. We obtain a joint model for the total energy and interatomic forcesthat follows fundamental quantum-chemical principles. This includesrotationally invariant energy predictions and a smooth, differentiablepotential energy surface. Our architecture achieves state-of-the-artperformance for benchmarks of equilibrium molecules and molecular dynamicstrajectories. Finally, we introduce a more challenging benchmark with chemicaland structural variations that suggests the path for further work.

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