We used Bayesian optimization to optimize the full hyperparameter feature space described in Table . We used the tree-structured Parzen estimator as the surrogate function, as implemented in the Python package Optuna version 3.0.3 which was also used to perform the optimizations. The optimization runs were initialized with data from the randomly sampled hyperparameter trial data set. The objective functions were estimated as the median from 20 bootstrap samples. Four different objective functions were used for each protein, two single objective and two dual-objective functions, these were 1. t 2 : the timescale dominant process, 2. VAMP2 eq (2) = 1 + λ 2 2 : the 'equilibrium' VAMP2 score of the 2 nd (dominant) process 3. t 2 and t 2 /t 3 : a multi-objective function of the timescale of the dominant process and the gap between the 2 nd and 3 rd timescale.