Bayesian optimization (BO) was employed to minimize hysteresis-based and error-based descriptor values. A gaussian process was used as the surrogate model. A Lower Confidence Bound acquisition function was used with an exploration-exploitation tradeoff parameter (λ) of 1 was selected, unless otherwise noted. Hyperparameters of the gaussian process, including kernel type and noise level, were estimated via maximum likelihood optimization at each iteration. The parameter domain was sampled on a log scale.