SchNet – A deep learning architecture for molecules and materials

Deep learning has led to a paradigm shift in artificial intelligence, including web, text, and image
search, speech recognition, as well as bioinformatics, with growing impact in chemical physics.
Machine learning, in general, and deep learning, in particular, are ideally suitable for representing
quantum-mechanical interactions, enabling us to model nonlinear potential-energy surfaces or
enhancing the exploration of chemical compound space. Here we present the deep learning architecture
SchNet that is specifically designed to model atomistic systems by making use of continuous-filter
convolutional layers. We demonstrate the capabilities of SchNet by accurately predicting a range of
properties across chemical space for molecules and materials, where our model learns chemically
plausible embeddings of atom types across the periodic table. Finally, we employ SchNet to predict
potential-energy surfaces and energy-conserving force fields for molecular dynamics simulations
of small molecules and perform an exemplary study on the quantum-mechanical properties of C20-
fullerene that would have been infeasible with regular ab initio molecular dynamics.