Markov state models (MSMs) are a popular model for extracting kinetic information from unbiased molecular dynamics simulations. Recent studies include a wide range of applications, such as understanding protein association kinetics, 1,2 enzyme dynamics, ion binding mechanisms, hydrogen bond dynamics, drug binding mechanisms for drug discovery, mutational effects on conformational dynamics, 12-15 kinetics of intrinsically disordered proteins, protein folding, and understanding allostery. Estimating an MSM proceeds by first collecting a data set of unbiased molecular dynamics (MD) simulations, then associating each molecular conformation with discrete states, counting transitions between states separated by the temporal resolution of the model (the lag time, τ ), and then deriving transition probabilities between states. The final model is summarized by the transition matrix T, where the elements T ij are the conditional probabilities of being in state i at time t and then transitioning to a state j at time t+τ : T ij (τ )=P (j, t=t+τ |i, t=t). The eigenvectors of the transition matrix represent the dynamic modes of the system as they relax to the equilibrium distribution.