  Deep Learning has been shown to learn efficient representations for
structured data such as image, text or audio. In this chapter, we present
neural network architectures that are able to learn efficient representations
of molecules and materials. In particular, the continuous-filter convolutional
network SchNet accurately predicts chemical properties across compositional and
configurational space on a variety of datasets. Beyond that, we analyze the
obtained representations to find evidence that their spatial and chemical
properties agree with chemical intuition.
