Accurate mechanistic description of structural changes in biomolecules is an increasingly important topic in structural and chemical biology. Markov models have emerged as a powerful way to approximate the molecular kinetics of large biomolecules while keeping full structural resolution in a divide-and-conquer fashion. However, the accuracy of these models is limited by that of the force fields used to generate the underlying molecular dynamics (MD) simulation data. Whereas the quality of classical MD force fields has improved significantly in recent years, remaining errors in the Boltzmann weights are still on the order of a few kT, which may lead to significant discrepancies when comparing to experimentally measured rates or state populations. Here we take the view that simulations using a sufficiently good force-field sample conformations that are valid but have inaccurate weights, yet these weights may be made accurate by incorporating experimental data a posteriori. To do so, we propose augmented Markov models (AMMs), an approach that combines concepts from probability theory and information theory to consistently treat systematic force-field error and statistical errors in simulation and experiment. Our results demonstrate that AMMs can reconcile conflicting results for protein mechanisms obtained by different force fields and correct for a wide range of stationary and dynamical observables even when only equilibrium measurements are incorporated into the estimation process. This approach constitutes a unique avenue to combine experiment and computation into integrative models of biomolecular structure and dynamics. molecular dynamics | integrative structural biology | maximum entropy | Markov state models | augmented Markov models A tomistic molecular dynamics (MD) simulation is a popular tool to investigate mechanisms underlying biomolecular function, including ligand binding (1, 2) and allostery (3), whereas coarse-grained molecular models are often used when studying assembly and interactions of supramolecular systems (4, 5). With recent advances in massively paralleled computation, simulating thousands of short-to medium-length realizations of many biomolecular systems has become feasible (6, 7). Systematic analysis of such large sets of MD data in terms of Markov state models (MSMs) (8, 9) now enables the study of slow dynamic processes, including protein folding (10, 11), conformational transitions (12, 13), and quantitative comparison with experiments (14-16) otherwise affordable only on specialpurpose supercomputers (17).Whereas these technologies are closing the gap as to which macromolecular systems and timescales can be directly simulated, it is becoming increasingly evident that systematic errors in empirical models-force fields-limit our ability to predict experimental data quantitatively (18). Although force fields are undergoing continuous improvement, high accuracy needs to be balanced with computational efficiency. A viable approach is to aim at force fields that are good enough such that...