FFLAME: a fragment-to-framework learning approach for MOF potentials

Metal-organic frameworks (MOFs) exhibit immense structural diversity and hold promise for applications ranging from gas storage and separation to energy storage and conversion. However, structural flexibility makes accurate and scalable property prediction difficult. While machine learning potentials (MLPs) offer a compelling balance between accuracy and efficiency, most existing models are system-specific and lack transferability across different MOFs. In this work, we introduce FFLAME - Fragment-to-Framework Learning Approach for MOF Potentials, a fragment-centric strategy for training transferable MLPs. By decomposing MOFs into their constituent metal clusters and organic linkers, FFLAME enables efficient reuse of chemical environments and significantly reduces the need for full-framework training data. We demonstrate that fragment-informed training improves model generalizability, particularly in data-scarce regimes, and accelerates convergence during fine-tuning. FFLAME achieves near-target accuracy on unseen MOFs with minimal additional training. These results establish a robust and data-efficient pathway toward general-purpose MLPs for the simulation of diverse framework materials.