where obs is an observed quantum chemical or physical property target to fit, and calc ( ) is the calculated value. of each target type was chosen to prevent the optimizer prioritizing one target type over other types. Carefully selected weights enabled the objective function contributions of different target types to have the same order of magnitude at the start of parameter optimization. is the regularization penalty weight, and Δ quantifies the deviation from a reference set of parameters -here, the initial SMIRNOFF99Frosst v 1.1.0 parameter set. Regularization ensures that parameter adjustments are made conservatively to avoid introducing large problematic parameter changes that may only provide marginal improvements in the optimization target, especially when smaller datasets are used in parameterization.