We reweight the first set of conformations to match the experimental R g data and resample them based on the weights to obtain a new set of 50,000 conformations. These resampled conformations are equilibrium samples from the reweighted ensemble Z ME 1 with equal weights and are used as the data samples in the potential contrasting step. The second set of conformations is used as the noise samples and the potential energy function U (x; θ 1 ) as the noise potential energy function. With the data samples, noise samples, and noise potential energy function defined as above, we optimize the hydrophobicity parameters θ using potential contrasting to obtain θ 2 . We then run temperature replica exchange MD simulations using θ 2 for each IDP in both the training and test sets and compare the predicted R g values with the experimental data. We can conduct more iterations of the proposed framework to further optimize the hydrophobicity parameters until the agreement between the experimental data and the simulated results stops improving. In this study, we run three more iterations of the proposed framework and obtain θ 2 , θ 3 , and θ 4 in the second, third, and fourth iterations, respectively. The predicted R g values using θ 2 , θ 3 , and θ 4 are compared with the experimental data in Fig. . The agreement between simulations and experiments is further improved in the second and third iterations and converges after the fourth. The optimized hydrophobicity parameters after each iteration are shown in Fig. and Table . After optimizing the parameters θ with one iteration of the proposed framework, we run temperature replica exchange MD simulations using the optimized parameters and compare the predicted R g values with the experimental data (Fig. and). The agreement between simulations and experiments is significantly improved for both the training and test IDPs.