The generation of 3D molecules requires simultaneously deciding the categorical
features~(atom types) and continuous features~(atom coordinates). Deep generative
models, especially Diffusion Models (DMs), have demonstrated effectiveness in generating
feature-rich geometries. However, existing DMs typically suffer from unstable probability
dynamics with inefficient sampling speed. In this paper, we introduce geometric flow
matching, which enjoys the advantages of both equivariant modeling and stabilized …

The generation of 3D molecules requires simultaneously deciding the categorical features
(atom types) and continuous features (atom coordinates). Deep generative models,
especially Diffusion Models (DMs), have demonstrated effectiveness in generating feature-
rich geometries. However, existing DMs typically suffer from unstable probability dynamics
with inefficient sampling speed. In this paper, we introduce geometric flow matching, which
enjoys the advantages of both equivariant modeling and stabilized probability dynamics …