SchNet - A Deep Learning Architecture for Molecules and Materials

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 are the first choice for images, the atoms in molecules are not restricted to a grid. Instead, their precise locations contain essential chemical information. Thus, we propose continuous-filter convolutional layers that we apply in SchNet: a novel deep learning architecture for modeling quantum interactions in molecules and materials [1]. Using filter-generating networks, we are able to encode prior knowledge about atom interactions, e.g. periodic boundary conditions, directly into the model. We predict chemical properties across compound space for molecules and materials as well as energy-conserving force fields for MD trajectories. Beyond achieving highly accurate predictions, SchNet provides spatially and chemically resolved insights into quantum-mechanical properties of atomistic systems beyond those trivially contained in the training set [1,2].

[1] K. T. Schütt et al., NIPS. (2017)
[2] K. T. Schütt et al., Nat. Comm. (2017)