tICA was performed at a range of lag times, with the tICA lag time at 10 ns used for both AMUSET-TICA and the microstate model. For AMUSEt, the pairwise distances were arranged according to their contributions to the first tICA eigenvector, with one Gaussian basis function per input feature centered at each feature's mean with a fixed covariance of 0.674. The tensor structure was built with 𝑁 5 = 6 and 𝑟 9': = 200. For AMUSET-TICA, we adopted six Gaussian basis for three input tICs and 𝜎 = 2, following the same approach as for alanine dipeptide and NLT9, which again yielded a rank-343 tensor-train structure. The reference implied timescales were obtained from the transition probability matrix of a 10,000-state model at 100 ns lag time, and the 10,000 states were again generated with K-Centers clustering on three tICs, but with the tICA lag time 10 ns. The same bootstrapping procedure was used to compute the implied timescale uncertainties as for the alanine dipeptide and NTL9 systems, using these implied scales from the 1000-state MSM as the "ground truth" reference for the Results.