Unlike conventional MSMs, which rely on predefined features and clustering to build transition matrices, VAMPnets learn these features from the data through a neural network architecture. This flexibility allows VAMPnets to capture more complex dynamical behaviors without requiring as much prior knowledge of the system. This procedure minimizes the error of a Koopman operator projected onto a lower-dimensional space, ensuring that the slowest dynamical modes of the system are preserved. In other words, VAMP conducts a singular value decomposition of the Koopman matrix, leveraging the Variational Principle; that the eigenvalues of the candidate transition matrix is the lower bound to the optimal process.