An alternative method for integrating simulations and experiments is optimizing force fields based on experimental data. This approach is more general, as the optimized force field can be directly used on other systems, potentially improving its accuracy across a broad spectrum of applications. Several techniques have been developed to optimize force fields using experimental data by minimizing the discrepancy between simulations and experiments. Achieving a satisfactory agreement between simulations and experiments typically necessitates multiple rounds of optimizations. Each round often requires extensive molecular dynamics simulations, thereby rendering the process computationally demanding, although strategies 17 that incorporate reweighting have been introduced to reduce to some extent the computational cost. Because the amount of experimental data is often limited and insufficient to uniquely determine the force field, regularization is needed in the optimization. Examples of regularization include restricting the force field to use specific functional forms or focusing the optimization on a subset of parameters. While such regularization is necessary, it can result in suboptimal force fields and potentially limit the extent of improvements achievable. Furthermore, the optimization process primarily focuses on aligning simulation results with experimental data, without considering the extent of changes made to the original ensemble. This could lead to force fields that are overly tailored to systems with experimental data, reducing their transferability and accuracy for other systems.