The training set RMSEs and objective functions for the 𝑁 = 10 runs are shown in Figure . . We note that the optimization initialized with 𝑁 = 10 initial parameter vectors improve the objective function with fewer iterations that the 𝑁 = 5 optimizations. These optimizations leverage the additional initial information to find improved parameter sets sooner, with the trade-off that more simulations are required upfront to achieve this result. The effectiveness of the optimization is also slightly improved over the 𝑁 = 5 optimizations, with an average objective function of 0.031 (𝑁 = 5: 0.039), an average 𝜌 𝐿 training set RMSE of 0.014 g/mL (𝑁 = 5: 0.016), and an average Δ𝐻 𝑣𝑎𝑝 RMSE of 2.38 kJ/mol (𝑁 = 5: 2.75 kJ/mol).