Building-Block Aware Generative Modeling for 3D Crystals of Metal Organic Frameworks

TL;DR

This paper introduces a novel generative model for 3D metal-organic frameworks that explicitly encodes building blocks, enabling the creation of diverse, valid, and novel MOFs with complex structures, and successfully synthesizes a predicted MOF.

Contribution

The paper presents BBA MOF Diffusion, a new SE(3)-equivariant diffusion model that learns detailed 3D representations of MOF building blocks, expanding chemical space exploration.

Findings

Successfully generates MOFs with 1000-atom unit cells.

Synthesized a high-scoring MOF predicted by the model.

Achieves high geometric validity, diversity, and novelty in generated MOFs.

Abstract

Metal-organic frameworks (MOFs) marry inorganic nodes, organic edges, and topological nets into programmable porous crystals, yet their astronomical design space defies brute-force synthesis. Generative modeling holds ultimate promise, but existing models either recycle known building blocks or are restricted to small unit cells. We introduce Building-Block-Aware MOF Diffusion (BBA MOF Diffusion), an SE(3)-equivariant diffusion model that learns 3D all-atom representations of individual building blocks, encoding crystallographic topological nets explicitly. Trained on the CoRE-MOF database, BBA MOF Diffusion readily samples MOFs with unit cells containing 1000 atoms with great geometric validity, novelty, and diversity mirroring experimental databases. Its native building-block representation produces unprecedented metal nodes and organic edges, expanding accessible chemical space by orders of magnitude. One high-scoring [Zn(1,4-TDC)(EtOH)2] MOF predicted by the model was synthesized, where powder X-ray diffraction, thermogravimetric analysis, and N2 sorption confirm its structural fidelity. BBA-Diff thus furnishes a practical pathway to synthesizable and high-performing MOFs.