Abstract

Large 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)} 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 =0.63pm 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 Dell 1.

Highlights

  • Large 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

  • The only method that satisfies these demands in principle is molecular dynamics (MD)[1,2], and it has been adopted in a wide variety of fields such as n­ anostructures[3] and b­ iochemistry[4,5]

  • More precise extraction of the critical exponent is not a scope of the current work, our results demonstrated that renormalized molecular dynamics (RMD) is capable of reproducing the critical behavior

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Summary

Introduction

Large 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. The recent advancement in parallel computing and introduction of coarse-graining methods have enabled large scale calculations, macroscopic analyses are still not realizable. Advancement in hardware, parallel computing methods as well as graphical processing units (GP-GPUs) have drastically increased the simulation scale ­achievable[6,7,8,9], but a majority of MD studies are still limited to the orders of micrometers and nanoseconds, and macroscopic analyses are often even impractical. In the fields of biochemistry and biophysics, common techniques are coarse-graining[10,11,12] and enhanced sampling m­ ethods[13,14,15] Another approach, often adopted for nanofluids and nanostructures, is hybrid methods of atomic-continuum d­ omains[16,17]

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