  This paper introduces a generative model equivariant to Euclidean symmetries:
E(n) Equivariant Normalizing Flows (E-NFs). To construct E-NFs, we take the
discriminative E(n) graph neural networks and integrate them as a differential
equation to obtain an invertible equivariant function: a continuous-time
normalizing flow. We demonstrate that E-NFs considerably outperform baselines
and existing methods from the literature on particle systems such as DW4 and
LJ13, and on molecules from QM9 in terms of log-likelihood. To the best of our
knowledge, this is the first flow that jointly generates molecule features and
positions in 3D.
