Recently, as part of the Open Force Field (OpenFF) Initiative, we have examined new methods of LJ parameter optimization. Central to these efforts is the development of the OpenFF Evaluator simulation workflow driver , which provides a standardized set of workflows for automatically building and executing physical property simulations for a given training or test data set. With the automation that this software provides, we can apply parameter optimization techniques to LJ parameters with minimal human intervention. In particular, this software enabled the application of the ForceBalance parameter optimization package to LJ parameters. Using regularized least squares optimization with the L-BFGS-B algorithm , we minimized an objective function that captures the ability of a parameter set to reproduce physical property observables. Using this framework, we studied the benefits of including physical property data of mixtures in training LJ parameters , then applied that training method to a production force field, OpenFF 2.0.0 (known also as Sage) .