Network measures of mixing.

Transport and mixing processes in fluid flows can be studied directly from Lagrangian trajectory data, such as those obtained from particle tracking experiments. Recent work in this context highlights the application of graph-based approaches, where trajectories serve as nodes and some similarity or distance measure between them is employed to build a (possibly weighted) network, which is then analyzed using spectral methods. Here, we consider the simplest case of an unweighted, undirected network and analytically relate local network measures such as node degree or clustering coefficient to flow structures. In particular, we use these local measures to divide the family of trajectories into groups of similar dynamical behavior via manifold learning methods.

Data-driven computation of molecular reaction coordinates.

The identification of meaningful reaction coordinates plays a key role in the study of complex molecular systems whose essential dynamics are characterized by rare or slow transition events. In a recent publication, precise defining characteristics of such reaction coordinates were identified and linked to the existence of a so-called transition manifold. This theory gives rise to a novel numerical method for the pointwise computation of reaction coordinates that relies on short parallel MD simulations only, but yields accurate approximation of the long time behavior of the system under consideration. This article presents an extension of the method towards practical applicability in computational chemistry. It links the newly defined reaction coordinates to concepts from transition path theory and Markov state model building. The main result is an alternative computational scheme that allows for a global computation of reaction coordinates based on commonly available types of simulation data, such as single long molecular trajectories or the push-forward of arbitrary canonically distributed point clouds. It is based on a Galerkin approximation of the transition manifold reaction coordinates that can be tuned to individual requirements by the choice of the Galerkin ansatz functions. Moreover, we propose a ready-to-implement variant of the new scheme, which computes data-fitted, mesh-free ansatz functions directly from the available simulation data. The efficacy of the new method is demonstrated on a small protein system.

Transition Manifolds of Complex Metastable Systems: Theory and Data-Driven Computation of Effective Dynamics.

We consider complex dynamical systems showing metastable behavior, but no local separation of fast and slow time scales. The article raises the question of whether such systems exhibit a low-dimensional manifold supporting its effective dynamics. For answering this question, we aim at finding nonlinear coordinates, called reaction coordinates, such that the projection of the dynamics onto these coordinates preserves the dominant time scales of the dynamics. We show that, based on a specific reducibility property, the existence of good low-dimensional reaction coordinates preserving the dominant time scales is guaranteed. Based on this theoretical framework, we develop and test a novel numerical approach for computing good reaction coordinates. The proposed algorithmic approach is fully local and thus not prone to the curse of dimension with respect to the state space of the dynamics. Hence, it is a promising method for data-based model reduction of complex dynamical systems such as molecular dynamics.

A Data-Driven Perspective on the Hierarchical Assembly of Molecular Structures.

Macromolecular systems are composed of a very large number of atomic degrees of freedom. There is strong evidence suggesting that structural changes occurring in large biomolecular systems at long time scale dynamics may be captured by models coarser than atomistic, although a suitable or optimal coarse-graining is a priori unknown. Here we propose a systematic approach to learning a coarse representation of a macromolecule from microscopic simulation data. In particular, the definition of effective coarse variables is achieved by partitioning the degrees of freedom both in the structural (physical) space and in the conformational space. The identification of groups of microscopic particles forming dynamical coherent states in different metastable states leads to a multiscale description of the system, in space and time. The application of this approach to the folding dynamics of two proteins provides a revised view of the classical idea of prestructured regions (foldons) that combine during a protein-folding process and suggests a hierarchical characterization of the assembly process of folded structures.

Understanding the geometry of transport: Diffusion maps for Lagrangian trajectory data unravel coherent sets.

Dynamical systems often exhibit the emergence of long-lived coherent sets, which are regions in state space that keep their geometric integrity to a high extent and thus play an important role in transport. In this article, we provide a method for extracting coherent sets from possibly sparse Lagrangian trajectory data. Our method can be seen as an extension of diffusion maps to trajectory space, and it allows us to construct "dynamical coordinates," which reveal the intrinsic low-dimensional organization of the data with respect to transport. The only a priori knowledge about the dynamics that we require is a locally valid notion of distance, which renders our method highly suitable for automated data analysis. We show convergence of our method to the analytic transfer operator framework of coherence in the infinite data limit and illustrate its potential on several two- and three-dimensional examples as well as real world data.

Commute Maps: Separating Slowly Mixing Molecular Configurations for Kinetic Modeling.

Identification of the main reaction coordinates and building of kinetic models of macromolecular systems require a way to measure distances between molecular configurations that can distinguish slowly interconverting states. Here we define the commute distance that can be shown to be closely related to the expected commute time needed to go from one configuration to the other, and back. A practical merit of this quantity is that it can be easily approximated from molecular dynamics data sets when an approximation of the Markov operator eigenfunctions is available, which can be achieved by the variational approach to approximate eigenfunctions of Markov operators, also called variational approach of conformation dynamics (VAC) or the time-lagged independent component analysis (TICA). The VAC or TICA components can be scaled such that a so-called commute map is obtained in which Euclidean distance corresponds to the commute distance, and thus kinetic models such as Markov state models can be computed based on Euclidean operations, such as standard clustering. In addition, the distance metric gives rise to a quantity we call total kinetic content, which is an excellent score to rank input feature sets and kinetic model quality.

Direct generation of loop-erased transition paths in non-equilibrium reactions.

A computational procedure is proposed to generate directly loop-erased transition paths in the context of non-equilibrium reactions, i.e. reactions that occur in systems whose dynamics is not in detailed balance. The procedure builds on results from Transition Path Theory (TPT), and it avoids altogether the need to generate reactive trajectories, either by brute-force calculations or using importance sampling schemes such as Transition Path Sampling (TPS). This is computationally advantageous since these reactive trajectories can themselves be very long and intricate in complex reactions. The loop-erased transition paths, on the other hand, are shorter and simpler because, by construction, they are pruned of all the detours typical reactive trajectories make and contain only their productive pieces that carry the effective current of the reaction. As a result they give direct access to the reaction rate and mechanism.