The thermodynamic constraint (pressure) exhibits a high degree of sensitivity to the choice of multi-objective optimization algorithms. Among all the coarse-graining optimizations performed, the primary source of MSE is attributed to pressure, with errors stemming from the probability distributions of bond, angle, dihedral, and radial distribution function (RDF) being comparatively minor. Trade-off between Sampling and Accuracy The effect of MD sampling on the optimized potentials is important for the iterative coarsegraining approach. Therefore, we conduct a detailed analysis to carefully investigate the influence of the MD sampling length on the performance of the resulting CG-PS model in the DiffCG framework. Figure (a) compares the total MSE E taotal during the coarse-graining optimization with various sampling lengths from t sim = 125 ps to t sim = 1000 ps. The decay rate of MSE along the optimization iterations is similar for different sampling lengths, while the CG optimization with longer sampling length achieves lower MSE, i.e., higher accuracy. The cumulative time for the 300 iterative optimization with various sampling lengths is shown in Figure (b). Obviously, optimization with a longer sampling requires a longer accumulative time. However, the difference between each other is not proportional to the sampling length due to the reweighting scheme in DiffTre (see a detailed discussion in Supporting Information). Figure (c) depicts the trade-off between accuracy and MD sampling length. The data from DiffCG using other multi-objective optimization algorithms are also included for comparison. The CoV-based DiffCG method surpasses all alternative approaches in achieving convergence towards the Pareto frontier. This analysis serves as a valuable reference to determine the appropriate sampling length in alignment with the desired level of accuracy. Figure (d) shows that optimizations with various sampling lengths converge to the same potentials within errors after 300 iterations. The converged potentials fluctuate slightly between iterations, which may explain the small differences that can be observed. A noticeable dependence of the simulation lengths is the optimized dihedral potentials. As seen in Figure , the longer simulation length yields a better reproduction of the probability distribution of dihedral angles. It can probably be attributed to the slow relaxation times of the dihedral angles in comparison to other degrees of freedom, e.g., bond length and bond angle. This insight suggests the potential incorporation of enhanced sampling techniques to expedite the sampling process during the coarse-graining optimization.