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Project B4: Equilibrium and non-equilibrium processes in open systems via adaptive resolution simulations Computational soft matter constitutes a major application area for simulations, with extraordinary conceptual and practical relevance. Due to the systems’ intrinsic complexity, a considerable effort in this area has focused on investigating somewhat idealised models, e.g., consisting of a few essential molecular species in explicit or implicit solvent. In reality, even the simplest experimentally relevant systems, such as (bio)macromolecules in aqueous mixtures and nanochannels, are far more complex, involving many interacting species, evolving under open-boundary and non-equilibrium conditions. Increasing the complexity and detail of the computational model for these systems poses a significant challenge. Indeed, the interplay of interactions and processes spanning a wide range of length and time scales requires a multiscale approach, including methods resolving quantum, classical, coarse-grained and continuum degrees of resolution. However, it is often the case that a high-resolution method is only […]

Project B6: Topological validation of coarse-grained polymer models Computational studies of polymer-based materials on large length and time scales require mesoscopic models: drastically coarse-grained descriptions where non-bonded potentials between interacting particles are on the order of the thermal energy. Such “soft” models are either used as “stand alone” descriptions or as elements of strategies, where the microscopic description of the material is recovered through sequential backmapping in a hierarchy of mesoscopic models. In the previous funding period, we focused on using single-chain topology ─ polymer knots ─ to validate mesoscopic models and hierarchical backmapping schemes for bulk high-molecular-weight polymer melts. We made three important findings: A) We demonstrated that polymer knots are, in general, multiscale objects, i.e. they simultaneously depend on microscopic and medium-scale features. As such, they cannot be always accurately described by mesoscopic models. B) Nevertheless, we identified conditions when mesoscopic models can quantitatively reproduce knotting properties of […]

Project B7: Automated model building and representation learning for multiscale simulations Project B7 addresses applications of machine learning techniques to multi-scale simulation of soft-matter systems. Multi-scale methods address the problem that the complexity of high-resolution base-line models grows too quickly for problems at relevant scales. Thus, they assumed that there is a coarser-resolution structure emerging from the details that can be efficiently computed with many fewer operations but that can still inform us about relevant behavioural aspects of the system. Machine learning can help in discovering such simplified surrogate computations by fitting a restricted computational model (such as a parametrized model, a kernel regressor, or a deep feed-forward network) to example results obtained from a full-resolution simulation. Conceptually, this involves two aspects: The first is to build a coarser-grained (CG) model. Learning of a CG model can take the form of just parametrizing a force-field or a mapping procedure motivated […]

Project A6: Coarse-grained models for dynamically asymmetric liquid mixtures under non-equilibrium conditions he main goal of this project is to gain better insight into the mapping of time-dependent properties of complex molecular systems, when studied using multiscale simulations. While the mapping of length scales is inherently defined by the coarse-graining procedure, the mapping of dynamic processes involves a complex combination of factors due to both the removal of degrees of freedom as well as approximations made in determining the coarse-grained (CG) interactions based on a reference all-atom (AA) model. As a consequence, the development of dynamically-consistent CG models is particularly challenging when various dynamic processes on different time scales coexist. To investigate these issues, we have focused on two important classes of systems, liquid crystals (LCs) and ionic liquids (ILs), which pair technological relevance with appropriate dynamics, and still show well defined modes of motion despite their significant complexity. In […]