Three foundational models, MACE-MP-0, SevenNet-0 and ORB-d3-v2, were evaluated against a validation set of 6000 structures, sampling a random 1000 frames from the equilibrated region of each MOF-temperature DFT dataset. These results are summarized in Table using a mean absolute error (MAE). These results rank foundational models, from best to worst, as ORB-d3-v2, MACE-MP-0 then SevenNet which roughly aligns with the MATBENCH ranking. 17 However all three foundational models are well above target accuracy. These targets are based on the level of error MLPs trained on MOF datasets achieved in literature and represent a near-DFT level accuracy, or quantum accuracy. We find that low temperature frames were predicted with less error than the high temperature structures (Supplementary Tables ). This suggests that a large contribution of the error is related to the foundational models being trained on relaxation trajectories.