Optimization against the "mixture-only" training was successful for both the 𝑁 = 10 and 𝑁 = 20 runs set, achieving significant reductions in objective function and training set RMSE, as shown in Figure . The 𝑁 = 20 optimization uses a total of 34 simulation evalutions to find its optimum, whereas the 𝑁 = 10 optimization uses only 17. In both optimizations we observe a large drop in objective, followed by incremental progress until the end of the optimization. The performance is slightly improved compared to the regularized simulation-only optimization; for the 𝑁 = 20 run the training set Δ𝐻 𝑚𝑖𝑥 RMSE is 0.19 kJ/mol (95% CI 0.16, 0.22) vs 0.24 kJ/mol (0.21, 0.29) for the simulation-only optimization. For 𝜌 𝐿 (𝑥), the RMSE is 0.011 g/ml (0.01, 0.013) vs 0.013 (0.011, 0.015). Both optimizations are significantly improved when compared to OpenFF 1.0.0, with Δ𝐻 𝑚𝑖𝑥 RMSE of 0.62 kJ/mol (0.54, 0.69) and 𝜌 𝐿 (𝑥) RMSE of 0.23 g/ml (0.02, 0.026).