FlexMOF: Joint Composition and All-Atom Coordinate Generation for Metal Organic Frameworks

Metal–organic frameworks (MOFs) are porous crystals assembled from metal nodes and organic linkers, offering tunable chemistry and pore structures for gas storage, carbon capture, and catalysis. Their vast design space presents an opportunity for AI-assisted discovery, but useful generation requires more than selecting building blocks: these building blocks must assemble into a periodic crystal with appropriate atomic conformations and pore geometry. Existing approaches often restrict the internal geometry of building blocks or learn composition independently of structure, limiting the connection between composition generation and its three-dimensional realization. We introduce FlexMOF, a unified model for joint composition and all-atom structure generation. Its coordinate diffusion process incorporates molecular topology and periodic interactions while allowing every atomic coordinate and the lattice to evolve. Building on this structural representation, joint training uses a composition encoder shared by the composition and structure pathways, together with latent features, to propagate coordinate supervision into the graph representation used for composition generation. This enables composition learning to benefit directly from modeling three-dimensional structures. FlexMOF achieves significantly better single-sample crystal structure recovery than the reported state-of-the-art all-atom baseline. Joint composition and structure generation also yields chemically and structurally valid, novel, and unique MOF candidates. By connecting flexible all-atom modeling with composition learning, FlexMOF represents an important step toward AI-assisted MOF discovery through the unified design of composition and all-atom periodic structure.