  Automating molecular design using deep reinforcement learning (RL) has the
potential to greatly accelerate the search for novel materials. Despite recent
progress on leveraging graph representations to design molecules, such methods
are fundamentally limited by the lack of three-dimensional (3D) information. In
light of this, we propose a novel actor-critic architecture for 3D molecular
design that can generate molecular structures unattainable with previous
approaches. This is achieved by exploiting the symmetries of the design process
through a rotationally covariant state-action representation based on a
spherical harmonics series expansion. We demonstrate the benefits of our
approach on several 3D molecular design tasks, where we find that building in
such symmetries significantly improves generalization and the quality of
generated molecules.
