  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.
More specifically, we propose a hybrid probability path where the coordinates
probability path is regularized by an equivariant optimal transport, and the
information between different modalities is aligned. Experimentally, the
proposed method could consistently achieve better performance on multiple
molecule generation benchmarks with 4.75$\times$ speed up of sampling on
average.
