x t and x t+τ respectively. Non-linear dimensionality reduction is then performed on these two sets of trajectory inputs for each time step, t. As per the VAMP principle, a VAMP-2 score is defined that attains its maximum value when the top left and right components of the Koopman operator K are equivalent to the subspaces spanned by these features. Deployed neural networks are trained to maximize the VAMP-2 score in order to achieve the best finitedimensional linear model. In the above process, it achieves the segregation of the trajectory frames and assigns them to particular clusters or Markov states, which then accelerates the process of rare-event sampling and transition state analysis. Key features for VAMPnets include the choice of the lag time, the number of output nodes, and the network depth of the architecture. Few pre-optimization runs with varying lag times were performed on the simulation trajectory to find the lag time that could resolve the slowest processes. The choice of a lag time relies upon the eigenvalue decomposition of the Markov propagators. When selecting a large lag time that exceeds the timescale of the slowest processes, it becomes difficult to fit the noisy data. However, a short lag time leads to them getting stuck in one of the suboptimal maxima of the training score. The selection of output nodes represents the separable metastable regions from the trajectory data. A higher number of output nodes may not be suitable for small trajectory data since the clustering will lead to discretizing the transition regions. The clustering of metastable states also depends heavily on the network depth of the architecture. A deeper network describes complex functions and is more difficult to train.