We use MACE, 34 a many-body equivariant graph neural network, that obtains state of the art performance on established benchmark datasets and extrapolation tasks. The training set was curated using an active learning protocol outlined in Reference [74]. New configurations are sampled from MD simulations, when the predicted relative force uncertainty of any atom, obtained from the discrepancy among five committee models, exceeds 50%. In this manner new configurations are iteratively added to the training set. The MLFF for KHCO 3 is trained from scratch, while the MFU-4l MLFF is fine-tuned from the MACE-MP-0 foundation model. During fine-tuning all weights in the model are allowed to change.