The workflow of the DiffCG is shown in Figure . All potential energy functions are represented by tabulated forms using cubic spline functions. As discussed in Section Structure-Based Coarse-Graining, the initial trial parameters are obtained via the direct Bolztamnn Inversion method to facilitate convergence of optimization. The optimization is carried out over 300 iterations using the stochastic gradient descent optimizer, i.e., Adam, implemented in Optax, with learning rate 0.003. The coarse-graining optimization is finished when it reaches the 300-iteration limit. This number is selected so that the total loss remains constant within a specified error range, and further optimization is unlikely to yield significant improvements. The initial weighting factors for the Uncertainty Weighting (Uncertainty)