  Metal-organic frameworks (MOFs) are of immense interest in applications such
as gas storage and carbon capture due to their exceptional porosity and tunable
chemistry. Their modular nature has enabled the use of template-based methods
to generate hypothetical MOFs by combining molecular building blocks in
accordance with known network topologies. However, the ability of these methods
to identify top-performing MOFs is often hindered by the limited diversity of
the resulting chemical space. In this work, we propose MOFDiff: a
coarse-grained (CG) diffusion model that generates CG MOF structures through a
denoising diffusion process over the coordinates and identities of the building
blocks. The all-atom MOF structure is then determined through a novel assembly
algorithm. Equivariant graph neural networks are used for the diffusion model
to respect the permutational and roto-translational symmetries. We
comprehensively evaluate our model's capability to generate valid and novel MOF
structures and its effectiveness in designing outstanding MOF materials for
carbon capture applications with molecular simulations.
