Markov state models (MSMs) and Master equation models are popular approaches to approximate molecular kinetics, equilibria, metastable states, and reaction coordinates in terms of a state space discretization usually obtained by clustering. Recently, a powerful generalization of MSMs has been introduced, the variational approach of conformation dynamics (VAC) and its special case the time-lagged independent component analysis (TICA), which allow us to approximate molecular kinetics and reaction coordinates by linear combinations of smooth basis functions or order parameters. While MSMs can be learned from trajectories whose starting points are not sampled from an equilibrium ensemble, TICA and VAC have as yet not enjoyed this property, and thus previous TICA /VAC estimates have been strongly biased when used with ensembles of short trajectories . Here, we employ Koopman operator theory and ideas from dynamic mode decomposition (DMD) to show how TICA /VAC can be used to estimate the unbiased equilibrium distribution from short-trajectory data and further this result in order to construct unbiased estimators for expectations, covariance matrices, TICA/VAC eigenvectors, relaxation timescales , and reaction coordinates .
Markov state models (MSMs) and Master equation models are popular approaches to approximate molecular kinetics, equilibria, metastable states, and reaction coordinates in terms of a state space discretization usually obtained by clustering. Recently, a powerful generalization of MSMs has been introduced, the variational approach (VA) of molecular kinetics and its special case the time-lagged independent component analysis (TICA), which allow us to approximate slow collective variables and molecular kinetics by linear combinations of smooth basis functions or order parameters. While it is known how to estimate MSMs from trajectories whose starting points are not sampled from an equilibrium ensemble, this has not yet been the case for TICA and the VA. Previous estimates from short trajectories, have been strongly biased and thus not variationally optimal . Here, we employ Koopman operator theory and ideas from dynamic mode decomposition (DMD) to extend the VA and TICA to non-equilibrium data. The main insight is that the VA and TICA provide a coefficient matrix that we call Koopman model, as it approximates the underlying dynamical (Koopman) operator in conjunction with the basis set used. This Koopman model can be used to compute a stationary vector to reweight the data to equilibrium. From such a Koopman-reweighted sample, equilibrium expectation values and variationally optimal reversible Koopman models can be constructed even with short simulations. The Koopman model can be used to propagate densities, and its eigenvalue decomposition provide estimates of relaxation timescales and slow collective variables for dimension reduction. Koopman models are generalizations of Markov state models, TICA and the linear VA and allow molecular kinetics to be described without a cluster discretization .
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of conformation dynamics (VAC)
(VA) of molecular kinetics
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molecular kinetics and reaction coordinates
slow collective variables and molecular kinetics
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MSMs can be learned
it is known how to estimate MSMs
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TICA and VAC have as yet not enjoyed this property, and thus previous TICA /VAC estimates
this has not yet been the case for TICA and the VA. Previous estimates from short trajectories,
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when used with ensembles of short trajectories
and thus not variationally optimal
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show how TICA /VAC
extend the VA and TICA to non-equilibrium data. The main insight is that the VA and TICA provide a coefficient matrix that we call Koopman model, as it approximates the underlying dynamical (Koopman) operator in conjunction with the basis set used. This Koopman model
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estimate the unbiased equilibrium distribution from short-trajectory data and further this result in order to construct unbiased estimators for expectations, covariance matrices, TICA/VAC eigenvectors, relaxation timescales , and reaction coordinates
compute a stationary vector to reweight the data to equilibrium. From such a Koopman-reweighted sample, equilibrium expectation values and variationally optimal reversible Koopman models can be constructed even with short simulations. The Koopman model can be used to propagate densities, and its eigenvalue decomposition provide estimates of relaxation timescales and slow collective variables for dimension reduction. Koopman models are generalizations of Markov state models, TICA and the linear VA and allow molecular kinetics to be described without a cluster discretization