The final model comprises nearly 1M trainable parameters, where this number is primarily defined by AB, INT, and RB. While we did not vary the CUTOFF, it is another crucial parameter to consider, especially for molecular clusters and their dynamics. Another option for properly incorporating long-range interactions would be using a different ML model that combines short-ranged interaction modeling with long-range/dispersion corrections. Further, for minimizing the loss function, we utilize the Adam optimizer with weight decay (AdamW) and L 2 regularization. The learning rate (LR) affects the step size of the gradient Conversely, larger batch sizes (typically limited by computer memory) can provide more accurate iteration during optimization and, thus, can be used for fine-tuning.