Project A2: Dynamically consistent coarse-grained models

The aim of this project is to develop methods that endow chemically-specific coarse-grained (CG) simulation models with consistent dynamical properties. To this end, CG models with conservative and dissipative interactions are derived from a higher-resolution model using bottom-up coarse-graining methods that retain a highmlevel of chemical specificity.

In the first two funding phases, we have developed methods for deriving Markovian and non-Markovian CG models that successfully represent the dynamics of molecular liquids, polymer solutions, and star-polymer melts on diffusive time scales.

The Markovian method uses a dissipative particle dynamics (DPD) thermostat that is parameterised by means of a bottom-up approach using the microscopic dynamics. While successful in CG simulations of molecular liquids where only the friction due to the relaxation of atomic vibrations needs to be accounted for, it fails to describe the dynamics of polymer melts and the dynamics of small molecules in polymer networks (polymer permeation) where memory times exceed the typical diffusion time of the particles. We addressed this issue in the second funding period and introduced a new non-Markovian simulation method based on a generalisedmLangevin (GLE) model that contains an isotropic memory kernel derived from Q-projected force correlation functions. This GLE model has been implemented using auxiliary variables and represents the dynamics ofmstar-polymer melts at a significantly reduced computational cost in comparison with non-Markovian DPD.

In the third funding period, we want to develop our new isotropic GLE method to operability for chemically-specific CG simulation models. To this end, better understanding of the interplay between conservative and dissipative forces is required to represent dynamics on all time scales. We plan to collaborate with project A6 on modelling the dynamics of ionic liquids with CG models that approximate the many-body potential of mean force (PMF) with varying accuracy. We furthermore plan to address the transferability issue following two distinct approaches. In the first approach, we want to augment the isotropic memory kernel in spatially inhomogeneous systems with a local-density dependence; in the second approach we want to derive (distance-dependent) pairwise friction kernels for Markovian and non-Markovian DPD. This project is complementary to project A3 . We plan to collaborate with A3 on aspects of implementing extended Markov systems for systems with memory, and on memory in inhomogeneous molecular systems such as ionic liquids studied in project A6 . The scope of the new methods developed in this project will be studied with applications to modelling penetrant/plasticiser dynamics in polymers and dynamics of ionic liquids at different thermodynamic state points.

Dynamical coarse-grained models of molecular liquids and their ideal and non-ideal mixtures
Madhusmita Tripathy, Viktor Klippenstein and Nico van der Vegt
J. Chem. Phys. 2023, 159(9)
see publication


Bottom-Up Informed and Iteratively Optimized Coarse-Grained Non-Markovian Water Models with Accurate Dynamics
Viktor Klippenstein and Nico van der Vegt
J. Chem. Theory Comput. 2023, 19(4), 1099-1110
see publication


Cross-correlation corrected friction in (generalized) Langevin models:Application to the continuous Asakura–Oosawa model
Viktor Klippenstein, Nico F. A. van der Vegt
Journal of Chemical Physics 157, 044103 (2022)
see publication


Cross-correlation corrected friction in (generalized) Langevin models
Viktor Klippenstein, Nico F. A. van der Vegt
The Journal of Chemical Physics 154 (19), 191102 (2021)
see publication


Introducing Memory in Coarse-Grained Molecular Simulations
Viktor Klippenstein, Madhusmita Tripathy, Gerhard Jung, Friederike Schmid, Nico F. A. van der Vegt
The Journal of Physical Chemistry B125 (19), 4931-4954 (2021)
see publication


Characterizing Polymer Hydration Shell Compressibilities with the Small-System Method
Madhusmita Tripathy, Swaminath Bharadwaj, Shadrack Jabes B., Nico F. A. van der Vegt
Nanomaterials 10 (8), 1460 (2020)
see publication


Conditional reversible work coarse-grained models with explicit electrostatics - An application to butylmethylimidazolium ionic liquids
Gregor Deichmann and Nico F. A. van der Vegt
Journal of Chemical Theory and Computation 15, 1187-1198 (2019)
see publication


Phase equilibria modeling with systematically coarse-grained models - A comparative study on state point transferability
Gregor Deichmann, Marco Dallavalle, David Rosenberger and Nico F. A. van der Vegt
The Journal of Physical Chemistry B123, 504-515 (2019)
see publication


Bottom-up approach to represent dynamic properties in coarse-grained molecular simulations
Gregor Deichmann and Nico F. A. van der Vegt
Journal of Chemical Physics 149, 244114 (2018)
see publication


Intrinsic conformational preferences and interactions in alpha-synuclein fibrils: Insights from molecular dynamics simulations
Ioana M. Ilie, Divya Nayar, Wouter K. den Otter, Nico F. A. van der Vegt, Wim J. Briels
Journal of Chemical Theory and Computation 14, 3298-3310 (2018)
see publication


Conditional Reversible Work Coarse-Grained Models of Molecular Liquids with Coulomb Electrostatics – A Proof of Concept Study on Weakly Polar Organic Molecules
Gregor Deichmann, Nico F. A. van der Vegt
Journal of Chemical Theory and Computation 13 (12), 6158-6166 (2017)
see publication


Study of Hydrophobic Clustering in Partially Sulfonated Polystyrene Solutions with a Systematic Coarse-Grained Model
Ran Zhang, Nico F. A. van der Vegt
Macromolecules 49 (19), 7571-7580 (2016)
see publication


Solid-liquid work of adhesion of coarse-grained models of n-hexane on graphene layers derived from the conditional reversible work method
Vikram Reddy Ardham, Gregor Deichmann, Nico F. A. van der Vegt, Frédéric Leroy
The Journal of Chemical Physics 143 (24), 243135 (2015)
see publication


Bottom-up derivation of conservative and dissipative interactions for coarse-grained molecular liquids with the conditional reversible work method
Gregor Deichmann, Valentina Marcon, Nico F. A. van der Vegt
The Journal of Chemical Physics 141 (22), 224109 (2014)
see publication