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From those, we will select the rho_pure_h_vap (Pure density and heat of vaporization) and the rho_x_h_mix (mixture density and enthalpy of mixing) training sets to differentiate between the pure and mixture properties. We will run 5 replicates of ForceBalance optimizations (using the same settings as in the mixture feasibility study) for each of these training sets. In addition to these training sets, we will also construct expanded training sets for these types of parameters (hopefully with roughly ~2x the data points, data allowing) in order to explore the effect of the number of training data points on reproducibility.

Variability of initial conditions

We are also interested in the reproducibility of optimizations given small changes in initial parameter values. The procedure for this will be to add small amounts of Gaussian noise (probably <=5% of initial values) and run several otherwise identical optimizations. This stage will be completed after the first stage (just exploring variability from stochastic simulation outputs).

Experimental Matrix

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