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Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
Published on: April 12, 2019
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Bottom-Up Informed and Iteratively Optimized Coarse-Grained Non-Markovian Water Models with Accurate Dynamics
Viktor Klippenstein1, Nico F A van der Vegt1
1Eduard-Zintl-Institut für Anorganische und Physikalische Chemie, Technische Universität Darmstadt, 64287Darmstadt, Germany.
Journal of Chemical Theory and Computation
|February 6, 2023
Summary
Coarse-grained simulations often misrepresent molecular dynamics. This study introduces a dynamic coarse-graining method with iterative optimization to accurately capture liquid water
Area of Science:
- Computational chemistry
- Molecular dynamics simulations
- Soft matter physics
Background:
- Coarse-grained (CG) molecular dynamics (MD) simulations often accelerate dynamics, misrepresenting molecular vibrations and diffusive motions.
- Generalized Langevin Equation (GLE) thermostats offer a potential solution when parameterized using fine-grained dynamics.
- Standard CG methods like iterative Boltzmann inversion or force matching focus on conservative interactions.
Purpose of the Study:
- To apply a bottom-up dynamic coarse-graining method based on the Mori-Zwanzig formalism for CG models of liquid water.
- To improve the accuracy of GLE memory kernels through iterative optimization of memory kernels (IOMK).
- To assess the impact of different CG potentials on the accuracy of the dynamic CG models.
Main Methods:
- Application of a bottom-up dynamic coarse-graining method utilizing the Mori-Zwanzig formalism.
- Parameterization of isotropic GLE memory kernels for CG water models.
- Iterative optimization of memory kernels (IOMK) for enhanced accuracy.
- Analysis of the velocity autocorrelation function and distinct Van Hove function.
Main Results:
- Accurate estimates of isotropic GLE memory kernels were obtained for CG water models.
- The iterative optimization of memory kernels (IOMK) significantly improved the accuracy of the velocity autocorrelation function within a few iterations.
- The presented methods successfully achieved an accurate representation of structural relaxation, as evidenced by the distinct Van Hove function.
- The study explored how different CG potentials influence the performance of the dynamic and iteratively optimized models.
Conclusions:
- The bottom-up dynamic coarse-graining method, combined with iterative optimization of memory kernels (IOMK), accurately reproduces the dynamics of liquid water.
- This approach effectively addresses the limitations of standard CG simulations in capturing molecular time scales.
- The findings provide a robust framework for developing accurate CG models for molecular liquids.
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