scholarly journals Coarse graining from variationally enhanced sampling applied to the Ginzburg–Landau model

2017 ◽  
Vol 114 (13) ◽  
pp. 3370-3374 ◽  
Author(s):  
Michele Invernizzi ◽  
Omar Valsson ◽  
Michele Parrinello
Keyword(s):  
Critical Point ◽  
Sampling Method ◽  
Coarse Graining ◽  
Coarse Grained ◽  
Atomistic Simulations ◽  
Enhanced Sampling ◽  
Ginzburg Landau ◽  
Coarse Grained Model ◽  
Unique Approach ◽  
Physical Features

A powerful way to deal with a complex system is to build a coarse-grained model capable of catching its main physical features, while being computationally affordable. Inevitably, such coarse-grained models introduce a set of phenomenological parameters, which are often not easily deducible from the underlying atomistic system. We present a unique approach to the calculation of these parameters, based on the recently introduced variationally enhanced sampling method. It allows us to obtain the parameters from atomistic simulations, providing thus a direct connection between the microscopic and the mesoscopic scale. The coarse-grained model we consider is that of Ginzburg–Landau, valid around a second-order critical point. In particular, we use it to describe a Lennard–Jones fluid in the region close to the liquid–vapor critical point. The procedure is general and can be adapted to other coarse-grained models.

THE SIMULATION OF POLYSTYRENE/NANOPARTICLES COMPOSITE MICROSPHERES USING DISSIPATIVE PARTICLE DYNAMICS

2013 ◽  
Vol 12 (02) ◽  
pp. 1250111 ◽  
Author(s):  
HAILONG XU ◽  
QIUYU ZHANG ◽  
HEPENG ZHANG ◽  
BAOLIANG ZHANG ◽  
CHANGJIE YIN
Keyword(s):  
Dissipative Particle Dynamics ◽  
Sodium Dodecyl ◽  
Coarse Graining ◽  
Particle Dynamics ◽  
Coarse Grained ◽  
Polystyrene Nanoparticles ◽  
Coarse Grained Model ◽  
Composite Microspheres ◽  
Polystyrene Matrix ◽  
Materials Studio

Dissipative particle dynamics (DPD) was initially used to simulate the polystyrene/nanoparticle composite microspheres (PNCM) in this paper. The coarse graining model of PNCM was established. And the DPD parameterization of the model was represented in detail. The DPD repulsion parameters were calculated from the cohesive energy density which could be calculated by amorphous modules in Materials Studio. The equilibrium configuration of the simulated PNCM shows that the nanoparticles were actually "modified" with oleic acid and the modified nanoparticles were embedded in the bulk of polystyrene. As sodium dodecyl sulfate (SDS) was located in the interface between water and polystyrene, the hydrophilic head of SDS stretched into water while the hydrophobic tailed into polystyrene. All simulated phenomena were consistent with the experimental results in preparation of polystyrene/nanoparticles composite microspheres. The effect of surface modification of nanoparticles on its dispersion in polystyrene matrix was also studied by adjusting the interaction parameters between the OA and NP beads. The final results indicated that the nanoparticles removed from the core of composite microsphere to the surface with increase of a OA-NP . All the simulated results demonstrated that our coarse–grained model was reasonable.

scholarly journals Deep learning the slow modes for rare events sampling

2021 ◽  
Vol 118 (44) ◽  
pp. e2113533118
Author(s):  
Luigi Bonati ◽  
GiovanniMaria Piccini ◽  
Michele Parrinello
Keyword(s):  
Sampling Method ◽  
Rare Events ◽  
Conformational Transition ◽  
Transfer Operator ◽  
Atomistic Simulations ◽  
Enhanced Sampling ◽  
Collective Variables ◽  
Long Time ◽  
Computational Resources

The development of enhanced sampling methods has greatly extended the scope of atomistic simulations, allowing long-time phenomena to be studied with accessible computational resources. Many such methods rely on the identification of an appropriate set of collective variables. These are meant to describe the system’s modes that most slowly approach equilibrium under the action of the sampling algorithm. Once identified, the equilibration of these modes is accelerated by the enhanced sampling method of choice. An attractive way of determining the collective variables is to relate them to the eigenfunctions and eigenvalues of the transfer operator. Unfortunately, this requires knowing the long-term dynamics of the system beforehand, which is generally not available. However, we have recently shown that it is indeed possible to determine efficient collective variables starting from biased simulations. In this paper, we bring the power of machine learning and the efficiency of the recently developed on the fly probability-enhanced sampling method to bear on this approach. The result is a powerful and robust algorithm that, given an initial enhanced sampling simulation performed with trial collective variables or generalized ensembles, extracts transfer operator eigenfunctions using a neural network ansatz and then accelerates them to promote sampling of rare events. To illustrate the generality of this approach, we apply it to several systems, ranging from the conformational transition of a small molecule to the folding of a miniprotein and the study of materials crystallization.

scholarly journals A Multiscale Coarse-grained Model of the SARS-CoV-2 Virion

2020 ◽  
Author(s):  
Alvin Yu ◽  
Alexander J. Pak ◽  
Peng He ◽  
Viviana Monje-Galvan ◽  
Lorenzo Casalino ◽  
...  
Keyword(s):  
Large Scale ◽  
Molecular Model ◽  
Structural Data ◽  
Coarse Grained ◽  
Atomistic Simulations ◽  
Current Status ◽  
Multiscale Approach ◽  
X Ray Crystallography ◽  
Coarse Grained Model ◽  
Viral Particles

AbstractThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the COVID-19 pandemic. Computer simulations of complete viral particles can provide theoretical insights into large-scale viral processes including assembly, budding, egress, entry, and fusion. Detailed atomistic simulations, however, are constrained to shorter timescales and require billion-atom simulations for these processes. Here, we report the current status and on-going development of a largely “bottom-up” coarse-grained (CG) model of the SARS-CoV-2 virion. Structural data from a combination of cryo-electron microscopy (cryo-EM), x-ray crystallography, and computational predictions were used to build molecular models of structural SARS-CoV-2 proteins, which were then assembled into a complete virion model. We describe how CG molecular interactions can be derived from all-atom simulations, how viral behavior difficult to capture in atomistic simulations can be incorporated into the CG models, and how the CG models can be iteratively improved as new data becomes publicly available. Our initial CG model and the detailed methods presented are intended to serve as a resource for researchers working on COVID-19 who are interested in performing multiscale simulations of the SARS-CoV-2 virion.Significance StatementThis study reports the construction of a molecular model for the SARS-CoV-2 virion and details our multiscale approach towards model refinement. The resulting model and methods can be applied to and enable the simulation of SARS-CoV-2 virions.

scholarly journals Importance of Interface in the Coarse-Grained Model of CNT /Epoxy Nanocomposites

Nanomaterials ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 1479 ◽  
Author(s):  
Ke Duan ◽  
Li Li ◽  
Fei Wang ◽  
Weishuang Meng ◽  
Yujin Hu ◽  
...  
Keyword(s):  
Polymer Nanocomposites ◽  
Cohesive Energy ◽  
Load Transfer ◽  
Interaction Strength ◽  
Coarse Graining ◽  
Thermomechanical Properties ◽  
Coarse Grained ◽  
Strength Parameter ◽  
Coarse Grained Model ◽  
Transfer Behavior

Interface interactions play a crucial role in determining the thermomechanical properties of carbon nanotubes (CNTs)/polymer nanocomposites. They are, however, poorly treated in the current multi-scale coarse-grained (CG) models. To develop suitable CG models of CNTs/polymer nanocomposites, we demonstrate the importance of two aspects for the first time, that is, preserving the interfacial cohesive energy and reproducing the interface load transfer behavior of all-atomistic (AA) systems. Our simulation results indicate that, for CNTs/polymer nanocomposites, the interface cohesive energy and the interface load transfer of CG models are generally inconsistent with their AA counterparts, revealing significant deviations in their predicted mechanical properties. Fortunately, such inconsistency can be “corrected” by phenomenologically adjusting the cohesive interaction strength parameter of the interface LJ potentials in conjunction with choosing a reasonable degree of coarse-graining of incorporated CNTs. We believe that the problem studied here is general for the development of the CG models of nanocomposites, and the proposed strategy used in present work may be applied to polymer nanocomposites reinforced by other nanofillers.

scholarly journals Single-molecule biophysics experiments in silico: Towards a physical model of a replisome

2021 ◽  
Author(s):  
Christopher Maffeo ◽  
Han-Yi Chou ◽  
Aleksei Aksimentiev
Keyword(s):  
Single Molecule ◽  
In Silico ◽  
Computational Models ◽  
Coarse Grained ◽  
Atomistic Simulations ◽  
Test Bed ◽  
Computing Power ◽  
Coarse Grained Model ◽  
Biomolecular Dynamics ◽  
Single Molecule Studies

AbstractThe interpretation of single-molecule experiments is frequently aided by computational modeling of biomolecular dynamics. The growth of computing power and ongoing validation of computational models suggest that it soon may be possible to replace some experiments out-right with computational mimics. Here we offer a blueprint for performing single-molecule studies in silico using a DNA binding protein as a test bed. We demonstrate how atomistic simulations, typically limited to sub-millisecond durations and zeptoliter volumes, can guide development of a coarse-grained model for use in simulations that mimic experimental assays. We show that, after initially correcting excess attraction between the DNA and protein, qualitative consistency between several experiments and their computational equivalents is achieved, while additionally providing a detailed portrait of the underlying mechanics. Finally the model is used to simulate the trombone loop of a replication fork, a large complex of proteins and DNA.

Developing Coarse-Grained Force Fields of Poly (methylmethacrylate-b-2-vinylpyridine) from Atomistic Simulation

2012 ◽  
Vol 562-564 ◽  
pp. 123-128 ◽  
Author(s):  
Bo Du ◽  
Zi Lu Wang ◽  
Xue Hao He
Keyword(s):  
Mechanical Properties ◽  
Force Field ◽  
Self Assembly ◽  
Atomistic Simulation ◽  
Polymer Melts ◽  
Coarse Grained ◽  
Atomistic Simulations ◽  
Inversion Method ◽  
Coarse Grained Model ◽  
Vinyl Pyridine

A coarse-grained force field for poly (methylmethacrylate-b-2-vinyl pyridine) is developed based on the Iterative Boltzmann Inversion method. The proposed coarse-grained model, successfully reproduced the properties of the polymer melts obtained from atomistic simulations, may provide an efficient way to study their mechanical properties and self-assembly behaviors.

scholarly journals Coarse-graining molecular dynamics: stochastic models with non-Gaussian force distributions

2019 ◽  
Vol 80 (1-2) ◽  
pp. 457-479 ◽  
Author(s):  
Radek Erban
Keyword(s):  
Molecular Dynamics ◽  
Linear Models ◽  
Coarse Graining ◽  
Md Simulations ◽  
Coarse Grained ◽  
Spatial And Temporal Scales ◽  
Coarse Grained Model ◽  
Non Gaussian ◽  
Low Dimensional ◽  
Cellular Behaviour

Abstract Incorporating atomistic and molecular information into models of cellular behaviour is challenging because of a vast separation of spatial and temporal scales between processes happening at the atomic and cellular levels. Multiscale or multi-resolution methodologies address this difficulty by using molecular dynamics (MD) and coarse-grained models in different parts of the cell. Their applicability depends on the accuracy and properties of the coarse-grained model which approximates the detailed MD description. A family of stochastic coarse-grained (SCG) models, written as relatively low-dimensional systems of nonlinear stochastic differential equations, is presented. The nonlinear SCG model incorporates the non-Gaussian force distribution which is observed in MD simulations and which cannot be described by linear models. It is shown that the nonlinearities can be chosen in such a way that they do not complicate parametrization of the SCG description by detailed MD simulations. The solution of the SCG model is found in terms of gamma functions.

scholarly journals Deep Learning for Variational Multi-Scale Molecular Modeling

2020 ◽  
Author(s):  
Jun Zhang ◽  
Yaokun Lei ◽  
Yi Isaac Yang ◽  
Yi Qin Gao
Keyword(s):  
Coarse Grained ◽  
Atomistic Simulations ◽  
Molecular Models ◽  
Enhanced Sampling ◽  
Physical Systems ◽  
Molecular Systems ◽  
Fine Grained ◽  
Multi Scale ◽  
Machine Learning Approach ◽  
Training Objective

Molecular simulations are widely applied in the study of chemical and bio-physical systems. However, the<br>accessible timescales of atomistic simulations are limited, and extracting equilibrium properties of systems<br>containing rare events remains challenging. Two distinct strategies are usually adopted in this regard: either<br>sticking to the atomistic level and performing enhanced sampling, or trading details for speed by leveraging<br>coarse-grained models. Although both strategies are promising, either of them, if adopted individually,<br>exhibits severe limitations. In this paper we propose a machine-learning approach to ally both strategies so<br>that simulations on different scales can benefit mutually from their cross-talks: Accurate coarse-grained (CG)<br>models can be inferred from the fine-grained (FG) simulations through deep generative learning; In turn, FG<br>simulations can be boosted by the guidance of CG models via deep reinforcement learning. Our method<br>defines a variational and adaptive training objective which allows end-to-end training of parametric<br>molecular models using deep neural networks. Through multiple experiments, we show that our method is<br>efficient and flexible, and performs well on challenging chemical and bio-molecular systems. <br>

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