To facilitate faster evaluation of complex objective functions, modelers often use surrogate models , which are meant to approximate an expensive function with a simpler alternative that captures most of the important information. Surrogate modeling techniques have been developed in response to diverse sets of scientific and engineering challenges, including geological modeling , engineering design , and chemical process modeling . A popular technique for surrogate modeling is Gaussian process (GP) surrogate modeling, which has seen adoption in many disciplines due to its simplicity and efficacy in data-sparse regimes. While these surrogates are not perfect imitations of the high-level response, we can construct them to a reasonable level of accuracy with a limited number of expensive evaluations. In the context of molecular simulation parameter optimization, Befort et al. demonstrated a method of optimizing LJ parameters by building GP surrogates based on physical properties and applied this method to several hydrofluorocarbons as well as ammonium perchlorate.