Wavelets as basis functions to represent the coarse-graining potential in multiscale coarse graining approach

Maiolo, Massimo; Vancheri, Alberto; Krause, Rolf; Danani, Andrea

doi:10.1016/j.jcp.2015.07.039

Cited by 9 publications

(15 citation statements)

References 28 publications

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“…Fig. 9 shows that our approach can preserve the force function well, while the Tikhonov regularization (17) reduces the energy of force function. The reason behind this is that for the Tikhonov method, the model minimizes u 2…”

Section: Hexanementioning

confidence: 94%

“…For 30 frames of the trajectory data, we applied the wavelet smoothing model (10) with h = 0.005 nm to derive the force function. It can be seen that compared with the Tikhonov (17) and Laplacian regularization (18) methods, our approach preserves the minima better, which is important for CG modeling. To assess the statistic errors in our modeling, we calculated 5 independent numerical results and each experiment randomly sampled 30 frames throughout the trajectory.…”

Section: One Site Coarse-grained Watermentioning

confidence: 95%

“…Das and Andersen [16] constructed hierarchical basis functions associated with the elastic net method to derive force functions. M. Maiolo et al [17] applied Daubechies' orthogonal wavelets to represent the coarse-graining potential. More recently, Schöberl et al [18] prescribed a probabilistic coarse-to-fine map and presented a datadriven coarse-graining scheme of atomistic ensembles in equilibrium.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

B-spline tight frame based force matching method

Yang

Zhu

Tong

et al. 2018

Journal of Computational Physics

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Section: Hexanementioning

confidence: 94%

Section: One Site Coarse-grained Watermentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

B-spline tight frame based force matching method

Yang

Zhu

Tong

et al. 2018

Journal of Computational Physics

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“…Moreover, this set-up is also used in estimation of non-parametric models, for example by specifying the basis set {φ i } to be splines, or wavelets, [56].…”

Section: Optimal Coarse-grained Dynamicsmentioning

confidence: 99%

Path-space variational inference for non-equilibrium coarse-grained systems

Harmandaris

Kalligiannaki

Katsoulakis

et al. 2016

Journal of Computational Physics

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In this paper we discuss information-theoretic tools for obtaining optimized coarse-grained molecular models for both equilibrium and non-equilibrium molecular simulations. The latter are ubiquitous in physicochemical and biological applications, where they are typically associated with coupling mechanisms, multi-physics and/or boundary conditions. In general the non-equilibrium steady states are not known explicitly as they do not necessarily have a Gibbs structure.The presented approach can compare microscopic behavior of molecular systems to parametric and non-parametric coarse-grained models using the relative entropy between distributions on the path space and setting up a corresponding path-space variational inference problem. The methods can become entirely data-driven when the microscopic dynamics are replaced with corresponding correlated data in the form of time series. Furthermore, we present connections and generalizations of force matching methods in coarse-graining with path-space information methods. We demonstrate the enhanced transferability of information-based parameterizations to different observables, at a specific thermodynamic point, due to information inequalities.We discuss methodological connections between information-based coarse-graining of molecular systems and variational inference methods primarily developed in the machine learning community. However, we note that the work presented here addresses variational inference for correlated time series due to the focus on dynamics. The applicability of the proposed methods is demonstrated on high-dimensional stochastic processes given by overdamped and driven Langevin dynamics of interacting particles.

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“…Maiolo et al [21] formulated the wavelet-based MSCG approach and demonstrated its robustness through studies modeling liquid water and methanol. In addition to the aforementioned sequential multiscale methods, concurrent multiscale simulation approaches have also been developed in the last decade, where the coupling between the atomistic and CG regions allows for "on-the-fly" particle exchange [22,23].…”

Section: Introductionmentioning

confidence: 99%

Multiresolution Modeling of Semidilute Polymer Solutions: Coarse-Graining Using Wavelet-Accelerated Monte Carlo

2017

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Abstract:We present a hierarchical coarse-graining framework for modeling semidilute polymer solutions, based on the wavelet-accelerated Monte Carlo (WAMC) method. This framework forms a hierarchy of resolutions to model polymers at length scales that cannot be reached via atomistic or even standard coarse-grained simulations. Previously, it was applied to simulations examining the structure of individual polymer chains in solution using up to four levels of coarse-graining (Ismail et al., J. Chem. Phys., 2005, 122, 234901 and Ismail et al., J. Chem. Phys., 2005, 122, 234902), recovering the correct scaling behavior in the coarse-grained representation. In the present work, we extend this method to the study of polymer solutions, deriving the bonded and non-bonded potentials between coarse-grained superatoms from the single chain statistics. A universal scaling function is obtained, which does not require recalculation of the potentials as the scale of the system is changed. To model semi-dilute polymer solutions, we assume the intermolecular potential between the coarse-grained beads to be equal to the non-bonded potential, which is a reasonable approximation in the case of semidilute systems. Thus, a minimal input of microscopic data is required for simulating the systems at the mesoscopic scale. We show that coarse-grained polymer solutions can reproduce results obtained from the more detailed atomistic system without a significant loss of accuracy.

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Wavelets as basis functions to represent the coarse-graining potential in multiscale coarse graining approach

Cited by 9 publications

References 28 publications

B-spline tight frame based force matching method

B-spline tight frame based force matching method

Path-space variational inference for non-equilibrium coarse-grained systems

Multiresolution Modeling of Semidilute Polymer Solutions: Coarse-Graining Using Wavelet-Accelerated Monte Carlo

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