In this work surrogate assisted optimization is utilized to calibrate predictive molecular
models, called force fields, used in molecular simulations to reproduce the liquid density of a
hydrofluorocarbon refrigerant molecule. A previous calibration workflow which relied on
Gaussian process regression models and large Latin hypercube samples to screen force
field parameter space is extended to include Bayesian optimization methods to efficiently
guide the search for force field parameters. In comparison to the previous work, the …