Here 𝑎 .1 and 𝜎 .1 are the mean and covariance of the 𝑗-th Gaussian basis, respectively. We generate the Gaussian basis functions by inspecting the marginal distributions of all input features. In practice, we observed that the distributions along each tIC are often bimodal, thus we usually choose two Gaussian basis functions for each coordinate. Furthermore, we stack the Gaussian basis functions to further expand their coverage of the conformational space. Specifically, we apply each Gaussian basis set to every input feature 𝜒 . , thus the number of Gaussian basis functions on each input variable is 𝑁 5 (𝑖) = 2𝑁 @ , and 𝑁 6 = .1 + 2𝑁 @ / , ' . In cases where input features exhibit more complicated distributions, one could choose a larger value for the number of Gaussian basis functions.