New frontiers in particle-based multiscale and coarse-grained modeling
- Tristan Bereau (Max Planck Institute for Polymer Research, Mainz, Germany)
- Joseph Rudzinski (Max Planck Institute for Polymer Research, Germany)
- Kurt Kremer (Max Planck Institut for Polymer Research, Mainz, Germany)
Registration for the workshop is now open. 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.
When applying, please send the following information:
- Title & abstract of a poster
- Short summary of the researcher's experience relating to the topic of the workshop
Registration deadline: June 1, 2018.
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.
 Noid, W. G. (2013). Perspective: Coarse-grained models for biomolecular systems. The Journal of chemical physics, 139(9), 09B201_1.
 Voth, G. A. (Ed.). (2008). Coarse-graining of condensed phase and biomolecular systems. CRC press.
 Peter, C., & Kremer, K. (2010). Multiscale simulation of soft matter systems. Faraday discussions, 144, 9-24.
 Rudzinski, J. F., Kremer, K., & Bereau, T. (2016). Communication: Consistent interpretation of molecular simulation kinetics using Markov state models biased with external information. The Journal of Chemical Physics, 144(5), 051102.
 Bowman, G. R., Pande, V. S., & Noé, F. (Eds.). (2013). An introduction to Markov state models and their application to long timescale molecular simulation (Vol. 797). Springer Science & Business Media.
 Lindorff-Larsen, K., Piana, S., Dror, R. O., & Shaw, D. E. (2011). How fast-folding proteins fold. Science, 334(6055), 517-520.
 Mullinax, J. W., & Noid, W. G. (2009). Extended ensemble approach for deriving transferable coarse-grained potentials. The Journal of Chemical Physics, 131(10), 104110.
 Rudzinski, J. F., Lu, K., Milner, S. T., Maranas, J. K., & Noid, W. G. (2017). Extended Ensemble Approach to Transferable Potentials for Low-Resolution Coarse-Grained Models of Ionomers. Journal of Chemical Theory and Computation, 13(5), 2185-2201.
 Bereau, T., Wang, Z. J., & Deserno, M. (2014). More than the sum of its parts: Coarse-grained peptide-lipid interactions from a simple cross-parametrization. The Journal of chemical physics, 140(11), 03B615_1-11220.
 Dalgicdir, C., Globisch, C., Sayar, M., & Peter, C. (2016). Representing environment-induced helix-coil transitions in a coarse grained peptide model. European Physical Journal Special Topics, 225.
 Menichetti, R., Kanekal, K. H., Kremer, K., & Bereau, T. (2017). In silico screening of drug-membrane thermodynamics reveals linear relations between bulk partitioning and the potential of mean force. The Journal of Chemical Physics, 147, 125101.
 Sanyal, T., & Shell, M. S. (2016). Coarse-grained models using local-density potentials optimized with the relative entropy: Application to implicit solvation. The Journal of chemical physics, 145(3), 034109.
 Lu, J., Qiu, Y., Baron, R., & Molinero, V. (2014). Coarse-graining of TIP4P/2005, TIP4P-Ew, SPC/E, and TIP3P to monatomic anisotropic water models using relative entropy minimization. Journal of Chemical Theory and Computation, 10(9), 4104-4120.
 Dama, J. F., Sinitskiy, A. V., McCullagh, M., Weare, J., Roux, B., Dinner, A. R., & Voth, G. A. (2013). The theory of ultra-coarse-graining. 1. General principles. Journal of chemical theory and computation, 9(5), 2466-2480.
 Potestio, R., Fritsch, S., Espanol, P., Delgado-Buscalioni, R., Kremer, K., Everaers, R., & Donadio, D. (2013). Hamiltonian adaptive resolution simulation for molecular liquids. Physical review letters, 110(10), 108301.
 Brini, E., Marcon, V., & van der Vegt, N. F. (2011). Conditional reversible work method for molecular coarse graining applications. Physical Chemistry Chemical Physics, 13(22), 10468-10474.
 Rudzinski, J. F., & Bereau, T. (2016). Concurrent parametrization against static and kinetic information leads to more robust coarse-grained force fields. The European Physical Journal Special Topics, 225(8-9), 1373-1389.
 Li, Z., Bian, X., Li, X., & Karniadakis, G. E. (2015). Incorporation of memory effects in coarse-grained modeling via the Mori-Zwanzig formalism. The Journal of chemical physics, 143(24), 243128.
 Jung, G., Hanke, M., & Schmid, F. (2017). Iterative Reconstruction of Memory Kernels. Journal of Chemical Theory and Computation.
 Bereau, T., & Kremer, K. (2016). Protein-Backbone Thermodynamics across the Membrane Interface. The Journal of Physical Chemistry B, 120(26), 6391-6400.
 Ghanbari, A., Böhm, M. C., & Müller-Plathe, F. (2011). A simple reverse mapping procedure for coarse-grained polymer models with rigid side groups. Macromolecules, 44(13), 5520-5526.
 John, S. T. (2016). Many-Body Coarse-Grained Interactions using Gaussian Approximation Potentials. arXiv preprint arXiv:1611.09123.
 Schöberl, M., Zabaras, N., P.S. Koutsourelakis (2017). Predictive coarse-graining. Journal of Computational Physics, 333, 49-77.