ForceBalance uses a Gauss-Newton approach to optimize the parameters by using the property gradients from Eq (7) to build the gradient and approximate the Hessian of the objective function. Here, we optimize θ to fit host-guest experimental binding data. The objective function in Eq (8) includes an L2 regularization penalty, and we use a prior width σ i of 0.5 Å for all radii.