Notably, the rank-343 AMUSET-TICA model reaches the converged ITS at a significantly shorter lag time compared to the rank-27 model. This observation is expected as the rank-27 model uses a smaller number of Gaussian basis functions, and thus results in larger numerical deviations in the predicted ITS at short lag times. The top three CVs identified by the rank-343 AMUSET-TICA model also clearly reveal the slowest transitions of alanine dipeptide (Figure ): CV1 (with the ITS at ~1.1ns) corresponds to the transition between the left-hand side region (alpha-beta structures) and the right-hand side region (coil and left-handed helix); CV2 (with the ITS at ~60 ps) corresponds to the transition between alpha and beta structures labelled as red and blue regions, respectively; and CV3 (with the ITS at ~20 ps) corresponds to the transition between coils and left-handed helices labelled as red and blue regions, respectively. AMUSET-TICA outperforms both AMUSEt and tICA in predicting the slowest ITS (see Figure ). Notably, tICA fails to correctly predict the slowest ITS, even at lag times as long as 50 ps (green line in the left panel of Figure ). We attribute the success of AMUSET-TICA over tICA to the following: Whereas tICA generates CVs through linear combinations of input features, AMUSET-TICA expands orthogonal tICs into overlapping Gaussian basis functions and stacks them together (i.e., applying all Gaussian basis functions to each tIC). This approach leads to a more effective embedding of the phase space (see Figure ) and improved performance in prediction of the slowest ITS.