that accurately reproduce the DFT results for a diverse set of crystalline materials. These models have undergone extensive pretraining on datasets with total DFT energies (E) and atomic forces (F), mainly sampling from geometry optimization trajectories such as MPtrj 41 . Among these, MACE-MP-0 model (referred to hereafter as MACE-MP for simplicity) stands out with its combination of the atomic cluster expansion for structure representation with equivariant and high body-order message passing graph neural networks for regression. The pretraining datasets for these models are dominated by inorganic compositions with fewer organic and organic-inorganic solids; however, MACE-MP model has shown reasonable preliminary results on MOF systems. Therefore, we have built on this to finetune a transferable MLFF, termed MACE-DAC, tailored to MOFs and their interaction with relevant gases. This analysis encompasses both the foundational MACE-MP model and its variant, MACE-MP-0b that incorporates the analytic Ziegler-Biersack-Littmark (ZBL) repulsive potential to remove pathologies in the PES at short distances. The mean absolute error (MAE) for the average of total PES data, denoted as 'All' in Figure (c) and Table , exhibits a notable reduction compared to the original foundation models with respect to interaction energy and force predictions. This improvement is mainly attributed to enhanced predictive accuracy in the repulsive region of the finetuning model, assisted by configurations sampled by GoldDAC dataset.