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.
Adversarial reverse mapping of equilibrated condensed-phase molecular structures
Machine Learning: Science and Technology 1,
045014
(2020);
doi:10.1088/2632-2153/abb6d4
Kernel-Based Machine Learning for Efficient Simulations of Molecular Liquids
Journal of Chemical Theory and Computation 16 (5),
3194-3204
(2020);
doi:10.1021/acs.jctc.9b01256
Contact
- Dr. Tristan Bereau
- Max Planck-Institut für Polymerforschung
- Ackermannweg 10
- D-55128 Mainz
- Tel: +49 6131 379478
- Fax: +49 6131 379340
- bereaug-ANUKlhPqX@jLqbvmpip-mainz.mpg.de
- http://www.mpip-mainz.mpg.de/~bereau/
- Prof. Dr. Michael Wand
- Institute of Informatics
- Universität Mainz
- Staudinger Weg 9
- D-55128 Mainz
- Tel: 06131-39-24061
- Fax: 06131-39-23534
- wandmTgg@QCuni-mainz.de