GP surrogate models are built with the botorch software package, version 0.6.0, which provides a convenient and extensible framework for building a large number of surrogates. Surrogate are constructed individually for each physical property in the test set, and objective functions are calculated from the surrogates' predictions of their respective physical properties; the surrogate does not predict the objective function directly. Surrogates are constructed from all of the simulation level evaluations available; e.g. if there are simulations of 20 physical properties with 10 different LJ parameters vectors, then our process builds 20 individual surrogate models, each using all 10 parameter vectors in their construction. All surrogates use a constant mean function and RBF (radial basis function) covariance kernel; independent length scales 𝑙 for each parameter are chosen using automatic relevance determination (ARD) . The length scales 𝑙 represent the variances in each parameter dimension of the radial basis function; essentially, the diagonal of the covariance matrix; all off-diagonal elements (covariances) are set to 0. If a physical property simulated at a given set of parameters finishes with errors, that set is omitted from surrogate building; additionally, any sets that have density measurements lower than 20% of the experimental value are omitted, as this likely indicates that the parameters have induced a phase change. However, this restriction did not affect the optimization, as parameters violating the density constraint were never produced through our optimizations.