HomeCasesPrediction of fluid viscosity of alkanes by reverse non-equilibrium molecular dynamics simulation

Prediction of fluid viscosity of alkanes by reverse non-equilibrium molecular dynamics simulation

Overview

From the perspective of curbing global warming, there is a growing demand for more energy efficient machinery and equipment such as automobiles and home appliances.

Since one way to save energy is to reduce friction loss of sliding parts, lowering viscosity is strongly necessary for fluids such as lubrication oil as well as lowering friction.

In the design of fluids, it is generally recognized that the method of molecular simulation is effective in predicting the viscosity of mixtures of more than two materials or novel fluids.

As an example, we introduce the result of viscosity calculations for common organic molecules of alkanes using PFP.

image

The model and method

As an example of viscosity calculation, pentane, a 5C normal alkane, was selected as a target. Prior to the calculation, a liquid structure of pentane was constructed.

50 molecules of pentane were randomly placed in a simulation cell, and the cell volume was adjusted to maintain the density constant by molecular dynamics MD) method with PFP.

Three simulation models with different initial configurations were prepared to check the reproductivity of the final result.

image

image
image

The reverse non-equilibrium molecular dynamics (RNEMD) method [1] was adapted for the purpose of calculating viscosity. This method forcibly induces velocity gradient in a liquid phase by exchanging momenta between atoms that have two opposite velocity signs, positive and negative, in two specific regions

The RNEMD simulations were performed for duration of 2×105 steps for each of the three prepared models with different initial configurations. The integration time and temperature were set to 1 fs and 298 K, respectively.

Results and discussion

image
image

This calculation was done for a simple organic compound, but our PFP possesses remarkable features of high accuracy and universality.

PFP is expected to be applied to physical property prediction of systems where a classical force field is not fixed or needs to be tuned such as ionic liquids and electrolytes.

Calculation Condition

image

References

[1] F. Müller-Plathe, Phys. Rev. E, 59, 4894 (1999). [2] J. R. Rumble, CRC Handbook of Chemistry and Physics, CRC Press/Taylor & Francis.
Contact Us
Go to List of Cases
Features
Features

Matlantis: 3 Key Features

Matlantis supports companies exploring innovative materials for a sustainable future.

Versatility

汎用性 / Versatile イメージ
Supports a wide range of elements and structures

Matlantis can simulate properties of molecules and crystal systems, including unknown materials. Any combinations of 55 elements are currently supported, and more elements will be added.

Speed

高速 / High Speed イメージ
Over 10,000x faster than conventional methods

The atomistic simulation tasks that take hours to months using density functional theory (DFT) on a high-performance computer can be finished in only a few seconds using Matlantis.

User-Friendliness

使いやすさ / Easy to Use イメージ
Just open your browser to run simulations

Thanks to the pre-trained deep learning model, physical property calculation library, and high-performance computing environment, no hardware or software installations are required for performing simulations. Unlike conventional machine learning potentials, Matlantis requires no data collection or training by users.

Go to Product Detail