As an alternative acceleration approach to our concurrent learning strategy for training a system specific model, we explore the use of a large pre-trained OC20 38 model. We focus on the EquiformerV2 39 model, which has demonstrated exceptional performance on the OC20 dataset and has recently excelled in transition state optimization and Hessian calculations. Initial tests with the pre-trained EquiformerV2 models (eq31M and eq153M parameters) indicated a need for fine-tuning due to differences in DFT parameters used for optimizing our adsorbates and slab compared to the ones to which the original model was fit, particularly the inclusion of van der Waals corrections (See Figures ).