Our proof-of-concept model only optimizes the density, reducing the problem to β i = ρ sim /ρ exp . The Bayesian optimization algorithm calculates a posterior distribution over the existing data set and iteratively proposes new force field parameters in such a way to attain a minimization of the cost function, argmin (C), and thereby reach a local minima. As long as a minimum has not been reached, after each iteration the new set of force field parameters is used to restart a simulation and hereby create a new data set, the results of which are then reiterated and passed in the cost function.