A ∆-model requires a low-level method to estimate the targeted property and an ML-model to predict the difference between the ground truth and the estimate. The CG forces sampled from the atomistic simulations (F AA ) were used as the target property for training the ∆-models used here. IBI, with and without pressure correction (IBI PC and IBI PC , respectively), was used as the low-level method, which resulted in two distinct ∆-models (∆ PC and ∆ PC respectively). DeePCG was adopted as the ML-model used to predict the difference between the IBI estimates and F AA . DeePCG was selected out of convenience, since its authors' have provided an opensource implementation and it has shown good performance in relatively challenging CG water simulations. The rationale for choosing IBI was that it is among the simplest algorithms with wide adoption , and that it incorporates structural information that is potentially complementary to the force information used to train DeePCG models. The performance of the IBI PC , IBI PC , and DeePCG models were also benchmarked for each system to serve as a baseline comparison.