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 …