Utilizing Machine Learning for Scale Bridging: From Atomistic to the Coarse Grained Level and Back

Multiscale simulations which combine atomistic and coarse-grained (CG) simulation models can overcome size and time scale limitations of purely atomistic approaches while retaining chemical/biological specificity. In this context, linking the simulation scales and assessing and improving the inevitable shortcomings of the lower resolution models remains an ongoing effort in which machine learning (ML) plays an increasingly important role.

Generally, in bottom-up coarse-graining, CG interactions are devised such that an accurate representation of a higher-resolution (e.g. atomistic) sampling of configurational phase space is achieved. Recently, traditional bottom-up methods have been complemented by machine learning (ML) approaches. ML methods can be used to derive or validate CG models by matching the sampling of a (relatively complex) free-energy surface as opposed to low-dimensional target functions/properties. For example, high-dimensional free energy surfaces can be extracted from atomistic simulations with the help of artificial neural networks (NN) - which can then be employed for simulations on a CG level of resolution. Secondly, ML methods can also be used to obtain low-dimensional representations of the sampling of phase space or to identify suitable collective variables that describe the states and the dynamics of a system.

This information can then be directly fed into the CG potentials or be used to identify optimal CG representations and learn CG interactions. Moreover, the so-obtained low dimensional representations enable us to assess the consistency of the sampling in models at different levels of resolution, to go back and forth between the scales and ultimately to enhance and improve the sampling of the systems.

This talk was given by Professor Christine Peter who studied Chemistry and Mathematics at the University of Freiburg, Germany. In 2003 she obtained her PhD at the ETH Zurich, subsequently she worked at the National Institutes of Health (Bethesda, MD) and the Max Planck Institute for Polymer Research (Mainz, Germany) as a postdoctoral researcher and as an Emmy Noether research group leader.

Since 2013 she is a full professor for Theoretical Chemistry at the University of Konstanz, Germany. The research focus of the group lies in multiscale simulation models and scale-bridging approaches and the development of analysis methods for simulation data. Areas of interest range from peptide folding and aggregation, protein-protein interactions, to biopolymer/mineral interfaces.