Interestingly, although AMUSEt also utilizes Gaussian basis functions, it fails to correctly predict the slowest timescale for lag times up to 50 ps (blue line in the left panel of Figure ). This is primarily because AMUSEt directly uses input features (specifically, 45 pairwise distances for alanine dipeptide, see Methods for details) to expand into Gaussian basis functions. Its implementation requires that these input distances be ordered such that the leading distances fully capture the slowest dynamical modes, which is a challenging task. Specifically, as illustrated in Scheme 1, AMUSEt applies the tensor product stepwise to perform basis expansion one distance at a time. This will eventually result in a matrix 𝝍 with dimension 2 OP × 𝑁 -, where 𝑁 -is the number of MD frames. This matrix is too large to fit into standard computer memory, necessitating truncation of its dimension (reducing the number of rows) after SVD at each step based on the cutoff 𝑟 9': = 100 (see Scheme 1). Consequently, distances ranked higher, especially those preceding truncation, contribute more significantly to the identified CVs. However, since each input distance represents a mixture of various dynamical modes, ranking them effectively to capture the slowest dynamical modes presents a significant challenge. In our test of AMUSEt for alanine dipeptide, we ranked the 45 input distances based on their contributions to the slowest tIC1, but this was still insufficient to accurately predict the slowest ITS (left panel of Figure ). In contrast, our AMUSET-TICA effectively addresses this issue by expanding orthogonal tICs into overlapping Gaussian basis functions rather than expanding the input features themselves.