where N is the number of states. Beyond Markov state models, a number of other methods have arising to generate projections of state space. These methods leverage the powerful automated statistical insight yielded from deep learning approaches. While some recent methods have arisen leveraging either graph neural networks or new master equations to identify features , one approach that has shown promising traction is VAMPnets . VAMPnets (Variational Approach for Markov Processes) uses deep learning to map high-dimensional molecular dynamics data onto a lower-dimensional space, facilitating the identification of slow collective variables and metastable states. VAMPnets directly optimize the variational score for dynamical models, called the VAMP-2 score, which is linked to the approximation quality of slow processes in the system.