The dispersion-repulsion interactions, almost always modeled with the Lennard-Jones (LJ) potential, have received the least attention in fitting, as they are typically trained against experimental physical property data , since obtaining dispersion-repulsion estimates from QM is difficult ]. This leads to challenges with curating appropriate sets of experimental physical property data from the literature, as well as the computational cost of simulating sets of physical property data with molecular dynamics. Most physical properties used in training, which include densities , enthalpies of vaporization , enthalpies of mixing , solvation free energies and dielectric constants , require equilibrium simulations in one or more phases, and in some cases may require alchemical simulation techniques . In conjunction with the need to train against larger datasets to ensure accuracy and transferability, this makes optimization of LJ parameters a difficult problem. Calculating a single objective function value in order to measure parameter fitness requires a large number of simulations, which can be difficult to coordinate and execute, especially depending on available computational resources. As a result, one can find many instances of LJ parameters in major force fields that have remained unchanged for more than 20 years, despite significant advancements in hardware, simulation software, and methodology in that time.