scholarly journals Renormalization group theory of molecular dynamics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Daiji Ichishima ◽  
Yuya Matsumura

AbstractLarge scale computation by molecular dynamics (MD) method is often challenging or even impractical due to its computational cost, in spite of its wide applications in a variety of fields. Although the recent advancement in parallel computing and introduction of coarse-graining methods have enabled large scale calculations, macroscopic analyses are still not realizable. Here, we present renormalized molecular dynamics (RMD), a renormalization group of MD in thermal equilibrium derived by using the Migdal–Kadanoff approximation. The RMD method improves the computational efficiency drastically while retaining the advantage of MD. The computational efficiency is improved by a factor of $$2^{n(D+1)}$$ 2 n ( D + 1 ) over conventional MD where D is the spatial dimension and n is the number of applied renormalization transforms. We verify RMD by conducting two simulations; melting of an aluminum slab and collision of aluminum spheres. Both problems show that the expectation values of physical quantities are in good agreement after the renormalization, whereas the consumption time is reduced as expected. To observe behavior of RMD near the critical point, the critical exponent of the Lennard-Jones potential is extracted by calculating specific heat on the mesoscale. The critical exponent is obtained as $$\nu =0.63\pm 0.01$$ ν = 0.63 ± 0.01 . In addition, the renormalization group of dissipative particle dynamics (DPD) is derived. Renormalized DPD is equivalent to RMD in isothermal systems under the condition such that Deborah number $$De\ll 1$$ D e ≪ 1 .

2021 ◽  
Author(s):  
Kai Xu ◽  
Lei Yan ◽  
Bingran You

Force field is a central requirement in molecular dynamics (MD) simulation for accurate description of the potential energy landscape and the time evolution of individual atomic motions. Most energy models are limited by a fundamental tradeoff between accuracy and speed. Although ab initio MD based on density functional theory (DFT) has high accuracy, its high computational cost prevents its use for large-scale and long-timescale simulations. Here, we use Bayesian active learning to construct a Gaussian process model of interatomic forces to describe Pt deposited on Ag(111). An accurate model is obtained within one day of wall time after selecting only 126 atomic environments based on two- and three-body interactions, providing mean absolute errors of 52 and 142 meV/Å for Ag and Pt, respectively. Our work highlights automated and minimalistic training of machine-learning force fields with high fidelity to DFT, which would enable large-scale and long-timescale simulations of alloy surfaces at first-principles accuracy.


2020 ◽  
Author(s):  
Jordi Juárez-Jiménez ◽  
Philip Tew ◽  
Michael o'connor ◽  
Salome Llabres ◽  
Rebecca Sage ◽  
...  

<p>Molecular dynamics (MD) simulations are increasingly used to elucidate relationships between protein structure, dynamics and their biological function. Currently it is extremely challenging to perform MD simulations of large-scale structural rearrangements in proteins that occur on millisecond timescales or beyond, as this requires very significant computational resources, or the use of cumbersome ‘collective variable’ enhanced sampling protocols. Here we describe a framework that combines ensemble MD simulations and virtual-reality visualization (eMD-VR) to enable users to interactively generate realistic descriptions of large amplitude, millisecond timescale protein conformational changes in proteins. Detailed tests demonstrate that eMD-VR substantially decreases the computational cost of folding simulations of a WW domain, without the need to define collective variables <i>a priori</i>. We further show that eMD-VR generated pathways can be combined with Markov State Models to describe the thermodynamics and kinetics of large-scale loop motions in the enzyme cyclophilin A. Our results suggest eMD-VR is a powerful tool for exploring protein energy landscapes in bioengineering efforts. </p>


2020 ◽  
Author(s):  
Jordi Juárez-Jiménez ◽  
Philip Tew ◽  
Michael o'connor ◽  
Salome Llabres ◽  
Rebecca Sage ◽  
...  

<p>Molecular dynamics (MD) simulations are increasingly used to elucidate relationships between protein structure, dynamics and their biological function. Currently it is extremely challenging to perform MD simulations of large-scale structural rearrangements in proteins that occur on millisecond timescales or beyond, as this requires very significant computational resources, or the use of cumbersome ‘collective variable’ enhanced sampling protocols. Here we describe a framework that combines ensemble MD simulations and virtual-reality visualization (eMD-VR) to enable users to interactively generate realistic descriptions of large amplitude, millisecond timescale protein conformational changes in proteins. Detailed tests demonstrate that eMD-VR substantially decreases the computational cost of folding simulations of a WW domain, without the need to define collective variables <i>a priori</i>. We further show that eMD-VR generated pathways can be combined with Markov State Models to describe the thermodynamics and kinetics of large-scale loop motions in the enzyme cyclophilin A. Our results suggest eMD-VR is a powerful tool for exploring protein energy landscapes in bioengineering efforts. </p>


2021 ◽  
Author(s):  
Kai Xu ◽  
Lei Yan ◽  
Bingran You

Force field is a central requirement in molecular dynamics (MD) simulation for accurate description of the potential energy landscape and the time evolution of individual atomic motions. Most energy models are limited by a fundamental tradeoff between accuracy and speed. Although ab initio MD based on density functional theory (DFT) has high accuracy, its high computational cost prevents its use for large-scale and long-timescale simulations. Here, we use Bayesian active learning to construct a Gaussian process model of interatomic forces to describe Pt deposited on Ag(111). An accurate model is obtained within one day of wall time after selecting only 126 atomic environments based on two- and three-body interactions, providing mean absolute errors of 52 and 142 meV/Å for Ag and Pt, respectively. Our work highlights automated and minimalistic training of machine-learning force fields with high fidelity to DFT, which would enable large-scale and long-timescale simulations of alloy surfaces at first-principles accuracy.


2020 ◽  
Vol 10 (1) ◽  
Author(s):  
P. M. Pieczywek ◽  
W. Płaziński ◽  
A. Zdunek

Abstract In this study we present an alternative dissipative particle dynamics (DPD) parametrization strategy based on data extracted from the united-atom molecular simulations. The model of the homogalacturonan was designed to test the ability of the formation of large-scale structures via hydrogen bonding in water. The extraction of coarse-grained parameters from atomistic molecular dynamics was achieved by means of the proposed molecule aggregation algorithm based on an iterative nearest neighbour search. A novel approach to a time-scale calibration scheme based on matching the average velocities of coarse-grained particles enabled the DPD forcefield to reproduce essential structural features of homogalacturonan molecular chains. The successful application of the proposed parametrization method allowed for the reproduction of the shapes of radial distribution functions, particle velocities and diffusivity of the atomistic molecular dynamics model using DPD force field. The structure of polygalacturonic acid molecules was mapped into the DPD force field by means of the distance and angular bond characteristics, which closely matched the MD results. The resulting DPD trajectories showed that randomly dispersed homogalacturonan chains had a tendency to aggregate into highly organized 3D structures. The final structure resembled a three-dimensional network created by tightly associated homogalacturonan chains organized into thick fibres.


2006 ◽  
Vol 978 ◽  
Author(s):  
Sheng D. Chao

AbstractCurrent large scale atomistic simulations remain too computationally demanding to be generally applicable to industrial and bioengineering materials. It is desirable to develop multiscale modeling algorithms to perform efficient and informative mesoscopic simulations. Here we present a multipolar expansion method to construct coarse grained force fields (CGFF) for polymer nanostructures and nanocomposites. This model can effectively capture the stereochemical response to anisotropic long-range interactions and can be systematically improved upon adding higher order terms. The coarse-graining procedure forms the basis to perform a hierarchy of multiscale simulations starting with the quantum chemistry calculations to coarse grained molecular dynamics, hopefully toward continuum modeling. We have applied this procedure to molecular clusters such as alkane, benzene, and fullerene. For liquid alkane, molecular dynamics simulations using the CGFF can reproduce the pair distribution functions using atomistic force fields. Molecular mechanics simulations using the CGFF can well reproduce the energetics of benzene clusters from quantum chemistry electronic structure calculations. Subtle anisotropy in the interaction potentials of the fullerene dimer using the Brenner force field can also be well represented by the model. It is promising this procedure can be standardized and further extended.


2020 ◽  
Author(s):  
Bart Coppens ◽  
Jiří Pešek ◽  
Bart Smeets ◽  
Herman Ramon

&lt;p&gt;Biofilms exhibit heavily increased antibiotic tolerance in comparison to planktonic bacteria, leading to chronic complications during infection. This increased tolerance originates from extracellular polymeric substances (EPS). By binding the antibiotics, they limit access of active compounds to target sites. Embedding the antibiotics in polymer nanoparticles (NPs) provides a promising strategy to deal with this inactivation mechanism. Antibiotic compounds are then protected from unwanted interaction with the biofilm matrix. However, diffusion and subsequently penetration of NPs in the biofilm becomes the limiting factor. Chemical surface modifications would then allow to modify NP interaction with the biofilm and mediate deeper penetration.&amp;#160;&lt;/p&gt; &lt;p&gt;We present a particle-based model to investigate how structural differences in the biofilm impact NP diffusion, which can later be used to evaluate performance of various NP surface properties. We model the structure of the biofilm, diffusion of low NP concentrations and their interaction with the biofilm. Spherocylindrical bacteria are seeded according to empirically-derived structural parameters such as cell-cell distance, vertical and radial alignment. Interactions with the EPS matrix are represented as spherical zones with higher effective viscosity around the bacteria. We then use this setup to study how differences in biofilm organization and differences in matrix viscosity influence NP penetration depth.&amp;#160;&lt;/p&gt; &lt;p&gt;We show that sterical interaction with the bacteria alone is insufficient to explain the slowdown in diffusion found in single particle tracking (SPT) experiments. Higher effective EPS viscosity leads to lower NP penetration, but spread of the EPS zones were found to lower NP penetration more. These results are consistent with literature.&amp;#160;&lt;/p&gt; &lt;p&gt;The method we present here is suitable to evaluate the diffusion and entrapment of NPs in small concentrations in a heterogeneous biofilm environment, taking interactions with EPS and structure of the biofilm into account. Organization of the bacteria and the nature of interaction with EPS can be spatially varied and NPs can actively change the environment. This setup can be used on large scale biofilms, in contrast to computational fluid dynamics approaches, where the amount of computational cells would outscale the number of particles in the simulation. This particle-based model additionally allows to model interactions between NPs such as aggregation. The current coarse graining method for interactions between EPS and NPs allows to increase scale with less strain on the computational cost. This model will provide a solid base to study the fate of nanoparticles in highly heterogeneous biofilms and provide suggestions for NP surface properties and increase success rate for nanomedicine development.&amp;#160;&lt;/p&gt;


2020 ◽  
Author(s):  
Jordi Juárez-Jiménez ◽  
Philip Tew ◽  
Michael o'connor ◽  
Salome Llabres ◽  
Rebecca Sage ◽  
...  

<p>Molecular dynamics (MD) simulations are increasingly used to elucidate relationships between protein structure, dynamics and their biological function. Currently it is extremely challenging to perform MD simulations of large-scale structural rearrangements in proteins that occur on millisecond timescales or beyond, as this requires very significant computational resources, or the use of cumbersome ‘collective variable’ enhanced sampling protocols. Here we describe a framework that combines ensemble MD simulations and virtual-reality visualization (eMD-VR) to enable users to interactively generate realistic descriptions of large amplitude, millisecond timescale protein conformational changes in proteins. Detailed tests demonstrate that eMD-VR substantially decreases the computational cost of folding simulations of a WW domain, without the need to define collective variables <i>a priori</i>. We further show that eMD-VR generated pathways can be combined with Markov State Models to describe the thermodynamics and kinetics of large-scale loop motions in the enzyme cyclophilin A. Our results suggest eMD-VR is a powerful tool for exploring protein energy landscapes in bioengineering efforts. </p>


Author(s):  
Michael P. Allen ◽  
Dominic J. Tildesley

Coarse-graining is an increasingly commonplace approach to study, as economically as possible, large-scale, and long-time phenomena. This chapter covers the main methods. Brownian and Langevin dynamics are introduced, with practical details of the solution of the modified equations of motion. Several techniques which aim to bridge the gap to the hydrodynamic regime are described: these include dissipative particle dynamics, multiparticle collision dynamics, and the lattice Boltzmann method. Several examples of program code are provided. In the last part of the chapter, the derivation of a coarse-grained potential from an atomistic one is considered using force-matching and structure-matching, and the limitations of these approaches are discussed.


2020 ◽  
Author(s):  
Jordi Juárez-Jiménez ◽  
Philip Tew ◽  
Michael o'connor ◽  
Salome Llabres ◽  
Rebecca Sage ◽  
...  

<p>Molecular dynamics (MD) simulations are increasingly used to elucidate relationships between protein structure, dynamics and their biological function. Currently it is extremely challenging to perform MD simulations of large scale structural rearrangements in proteins that occur on millisecond timescales or beyond, as this requires very significant computational resources, or the use of cumbersome ‘collective variable’ enhanced sampling protocols. Here we describe a framework that combines ensemble MD simulations and virtual-reality visualization (eMD-VR) to enable users to interactively generate realistic descriptions of large amplitude, millisecond timescale protein conformational changes in proteins. Detailed tests demonstrate that eMD-VR substantially decreases the computational cost of folding simulations of a WW domain, without the need to define collective variables a priori. We further show that eMD-VR generated pathways can be combined with Markov State Models to describe the thermodynamics and kinetics of large scale loop motions in the enzyme cyclophilin A. Our results suggest eMD-VR is a powerful tool for exploring protein energy landscapes for bioengineering efforts. </p>


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