MOFFlow: Flow Matching for Structure Prediction of Metal-Organic Frameworks

Metal-organic frameworks (MOFs) are a class of crystalline materials with promising applications in many areas such as carbon capture and drug delivery. To address this limitation, we propose a novel Riemannian flow matching framework that reduces the dimensionality of the problem by treating the metal nodes and organic linkers as rigid bodies, capitalizing on the inherent modularity of MOFs. Metal-organic frameworks (MOFs) are a class of crystalline materials that have recently received significant attention for their broad range of applications, including gas storage (Li et al., 2018), gas separations (Qian et al., 2020), catalysis (Lee et al., 2009), drug delivery (Horcajada et al., 2012), sensing (Kreno et al., 2012), and water purification (Haque et al., 2011). They are particularly valued for their permanent porosity, high stability, and remarkable versatility due to their tunable structures. In particular, MOFs are tunable by adjusting their building blocks, i.e., metal nodes and organic linkers, to modify pore size, shape, and chemical characteristics to suit specific applications (Wang et al., 2013). Consequently, there is a growing interest in developing automated approaches to designing and simulating MOFs using computational algorithms. Crystal structure prediction (CSP) is a task of central importance for automated MOF design and simulation.