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B7 (N): Machine learning for multiscale simulations

The goal of the project is to examine the application of machine learning methods, in particular modern, multi-scale representation learning methods based on deep neural networks to multi-scale simulations of soft-matter systems. We will consider two application scenarios: (i) Regression to compute complex interactions: here we will use polarization as a test case for a non-additive interaction, and develop a regression scheme that learns a mapping function from the configuration of particles directly to forces. (ii) Generative probabilistic models: we will consider the problem of back-mapping coarse-grained simulations to higher-resolution representations. From a machine learning perspective, this requires conditional generative statistical models.

Funding for this project has started in July 2018.

Kernel-Based Machine Learning for Efficient Simulations of Molecular Liquids
Christoph Scherer, René Scheid, Denis Andrienko, Tristan Bereau
Journal of Chemical Theory and Computation 16 (5), 3194-3204 (2020);


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