By introducing this regularization loss, the finetuning process is guided to adapt more gradually to the new MOF data while retaining the knowledge learned from pretraining on inorganic materials. As shown in Figure (c), this method prevents performance degradation in the equilibrium and weak-attraction regions, observed in the finetuning model without the continual learning loss (termed as MACE-DAC-0), while maintaining the trend that significantly reduces loss in the repulsion region. Therefore, the continual learning model (termed as MACE-DAC-1) achieved the lowest MAE across all PES data.