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Prof. Dr. Davide Donadio Max Planck-Institut für Polymerforschung moved to UC Davis 163A Chemistry 95616, USADavis, CA Tel: +1 (530) 754 1040 Mail: ddonadio@ucdavis.edu Further information

Prof. Dr. Michael Wand Institute of Informatics Universität Mainz Staudinger Weg 9 D-55128 Mainz Tel: +49 6131 3924061 Fax: +49 6131 3923534 Mail: wandm@uni-mainz.de Further information

Prof. Dr. Andreas Hildebrandt Institut für Informatik Universität Mainz Staudingerweg 9 D-55128 Mainz Tel: +49 6131 39 23334 Fax: +49 6131 39 23534 Mail: andreas.hildebrandt@uni-mainz.de Further information

Project C4 (Completed): Coarse-graining frequency-dependent phenomena and memory in colloidal systems Electrostatic interactions can strongly influence the behavior of macromolecular systems. A particular challenge for their prediction is the accurate, albeit computationally tractable, handling of the influence of water dipoles on the potentials. To address this challenge, we develop an efficient and accurate numerical framework for nonlocal electrostatics of large molecular systems. An improved understanding of the influence of water structure on electrostatics has far-reaching applications: the results of the project can, in principle, be used wherever implicit water models are desired, but where a simple structureless continuum is insufficiently accurate. This project has ended in June 2018.

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 […]