A major current challenge poses the systematic construction of coarse-grained models that are dynamically consistent, and, moreover, might be used for systems driven out of thermal equilibrium. Here we present a novel prescription that extends the Markov state modelling approach to driven systems. The first step is to construct a complex network of microstates from detailed atomistic simulations with transition rates that break detailed balance. The coarse-graining is then carried out in the cycle space of this network. To this end we introduce the concept of representatives, which stand for many cycles with similar properties. We show how to find these cycle communities using welldeveloped standard algorithms. Removing all cycles except for the representatives defines the coarse-grained model, which is mapped back onto a network with far fewer states and renormalized transition rates that, however, preserve the entropy production of the original network. Our approach is illustrated and validated for a single driven particle.

Plasmonic gold nanoparticles are normally used as sensor to detect analytes permanently bound to their surface. If the interaction between the analyte and the nanosensor surface is negligible, it only diffuses through the sensor's sensing volume, causing a small temporal shift of the plasmon resonance position. By using a very sensitive and fast detection scheme, we are able to detect these small fluctuations in the plasmon resonance. With the help of a theoretical model consistent with our detection geometry, we determine the analyte's diffusion coefficient. The method is verified by observing the trends upon changing diffusor size and medium viscosity, and the diffusion coefficients obtained were found to reflect reduced diffusion close to a solid interface. Our method, which we refer to as NanoPCS (for nanoscale plasmon correlation spectroscopy), is of practical importance for any application involving the diffusion of analytes close to nanoparticles.

We report on an efficient and highly controlled cold atom hollow-core fiber interface, suitable for quantum simulation, information, and sensing. The main focus of this manuscript is a detailed study on transporting cold atoms into the fiber using an optical conveyor belt. We discuss how we can precisely control the spatial, thermal, and temporal distribution of the atoms by, e.g., varying the speed at which the atoms are transported or adjusting the depth of the transport potential according to the atomic position. We characterize the transport of atoms to the fiber tip for these different parameters. In particular, we show that by adapting the transport potential we can lower the temperature of the transported atoms by a factor of 6, while reducing the transport efficiency only by a factor 2. For atoms transported inside the fiber, we can obtain a transport efficiency into the fiber of more than 40% and we study the influence of the different transport parameters on the time-dependent optical depth signal. When comparing our measurements to the results of a classical transport simulation, we find a good qualitative agreement.

We describe a systematic approach to construct coarse-grained Markov state models from molecular dynamics data of systems driven into a non-equilibrium steady state. We apply this method to study the globule-stretch transition of a single tethered model polymer in shear flow. The folding/unfolding rates of the coarse-grained model agree with the original detailed model. We demonstrate that the folding/unfolding proceeds through the same narrow region of configuration space but along different cycles.

Molecular dynamics simulations allow to study the structure and dynamics of single biomolecules in microscopic detail. However, many processes occur on time scales beyond the reach of fully atomistic simulations and require coarse-grained multiscale models. While systematic approaches to construct such models have become available, these typically rely on microscopic dynamics that obey detailed balance. In vivo, however, biomolecules are constantly driven away from equilibrium in order to perform specific functions and thus break detailed balance. Here we introduce a method to construct Markov state models for systems that are driven through periodically changing one (or several) external parameter. We illustrate the method for alanine dipeptide, a widely used benchmark molecule for computational methods, exposed to a time-dependent electric field.