We encourage researchers working in topics directly related to the workshop to apply. There is no registration fee. However, due to limited space, we will favor applicants willing to contribute a poster relevant to the topic of the workshop. In the spirit of CECAM workshops, our workshop will dedicate significant time for discussions, and we hope to attract participants willing to actively participate in those.
Registration for the workshop is now closed.
Particle-based computer simulations numerically integrate the time evolution of a system based on the interactions between its constituents. They offer the possibility to model the emerging complexity of phenomena occurring over many length- and time-scales. While an atomistic description can offer detailed insight, a thorough sampling of the relevant conformational space remains challenging for all but the smallest of systems. These limitations have motivated the development of coarse-grained (CG) models, where multiple atoms are lumped into one particle or bead [1, 2]. Coupling several models forms the basis of a multiscale approach, where models of different resolutions probe different length- and time-scales .
The main challenges in multiscale and CG modeling include representability and transferability. Representability describes the extent to which the model can reproduce various properties of the original system. Transferability refers to the model’s accurate behavior beyond the state point or chemical composition it was parametrized from. Predictive modeling requires both aspects. Certain common assumptions that go into building CG models have imposed stringent constraints on the accuracy, e.g., the use of pairwise nonbonded potentials to reproduce the many-body potential of mean force.
While the modeling of structural, equilibrium properties has improved significantly over the last few decades, dynamics remains problematic. A CG model’s smoother energy landscape leads to reduced molecular friction, accelerating arbitrarily the different kinetic processes. As a result, CG models are typically much faster, but with inconsistent dynamics . In parallel, recent technological and algorithmic developments (e.g., specific hardware or distributed computing) have allowed to probe extremely long time-scales of certain complex systems from atomistic simulations [5, 6]. This further hinders the impact of coarse-graining, due to ever-increasing interests in kinetic properties.
This workshop will address current methodological avenues to push forward coarse-graining and multiscale approaches. We note specific examples that illustrate recent developments:
- State-point transferability, e.g., extended ensemble methods [7, 8]
- Transferability challenges in modeling structure formation [9, 10]
- The role of coarse-graining in reducing the size of chemical compound space 
- Improved transferability from interaction potentials beyond the pairwise assumption, e.g., density-based potential , three-body interactions 
- Incorporating hidden degrees of freedom, e.g., ultra coarse-graining 
- Adaptive resolution simulations 
- Improved transferability from more accurate description of the thermodynamics, e.g., conditional reversible work 
- Improved dynamics, e.g., Markov state models , memory kernels [18, 19]
- Linking the scales, e.g., backmapping [20, 21]
- Data-driven approaches for more accurately incorporating many-body effects  and uncertainty due to information loss  into coarse-grained models.
The workshop will take place at the Max Planck Institute for Polymer Research in Mainz, Germany. All events will be located in the Staudinger auditorium.
For directions on how to get to the MPIP, see: