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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 […]