In industrial applications, lubricants are key to the performance of a wide range of equipment. These chemicals can reduce the wear and tear of machinery and improve their efficiency by reducing friction between surfaces.  

Viscosity is one of the most important properties of a lubricant, since it is directly related to the behavior and performance of the lubricant in the application of choice. This property is mainly determined by the lubricant’s chemical composition and the conditions of temperature and pressure it is under.  

Therefore, the development of novel lubricants requires studying their viscosity under a range of different conditions that represent the needs of the target industrial application.

System of interest: DEHA

The lubricant studied in this use case is di(2-ethylexyl) adipate (DEHA). This chemical is a synthetic ester often used as a component of aircraft lubricants as well as a hydraulic fluid.  

Synthetic oil lubricants such as DEHA are valued for their ability to perform under extreme conditions. This makes them suitable for industrial applications that require high temperature and pressure, for example in the bearings of aircraft engines, where lubricants must withstand temperatures over 200 °C for long periods of time.  

However, the development of these high-performance lubricants can be challenging. The R&D process requires extensive trial and error due to the complexity of the lubricants and the fact that not much is known about their atomistic behavior.  

Here is where molecular dynamics simulations can offer an alternative to investigate the properties of new lubricants, particularly their viscosity. This methodology has been shown to deliver reliable estimates of the viscosity of mixtures of lubricants relevant to industrial applications at different temperatures.  

Use case: Viscosity simulations of DEHA

With just a few clicks, QuantistryLab allows the user to run molecular dynamics simulations of chemical systems such as lubricants, polymers, and electrolytes, to model and predict many of their macroscopic properties, including viscosity.  

To predict the viscosity of a lubricant, QuantistryLab deploys molecular dynamics simulations to measure fluctuations in the stress tensor over time. Using the Green-Kubo approach, a statistical mechanics method that employs autocorrelation functions of the stress tensor, an estimation of the viscosity at equilibrium is obtained.  

Running these simulations is straightforward with QuantistryLab's click&simulate technology. The first step is to generate a model of the lubricant of interest, which can be easily achieved by creating a liquid preparation and selecting the desired chemicals from the compound library. This pre-processed model serves as the starting point for the subsequent equilibration and simulation steps.

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For this use case, the lubricant preparation consists of 100% DEHA. Once the lubricant model has been created, its viscosity can be calculated by starting a workflow with QuantistryLab’s viscosity feature. The user simply needs to set the desired temperature and pressure for the simulation.  

The results of the simulation yield a 3D atomistic model that represents the dynamical behavior of the atoms that make up the lubricant preparation.

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The simulation workflow was run on three separate instances at different temperatures to study how the viscosity of DEHA changes under a range of conditions. Measurements were taken at 293 °K (~20 °C), 343 °K (~70 °C), and 393 °K (~120 °C).

The results obtained using QuantistryLab were then compared with experimental values of DEHA viscosity reported in peer reviewed literature. The values are shown in the figure below.

The comparison between experimental measurements and QuantistryLab’s simulations of viscosity demonstrated a high degree of agreement, accurately reproducing the experimental trend across different temperatures and providing quantitative estimations of the single values. This highlights QuantistryLab's capability to deliver accurate results, significantly reducing the need for numerous, costly and time-consuming experiments.

Chemical simulations are particularly valuable in R&D when it comes to complex chemicals including high-performance lubricants such as DEHA. In industrial applications, lubricants are often composed of a mixture of multiple lubricant chemicals and additives, which further complicates measurements of properties such as viscosity for different compositions and at a wide range of temperature and pressure conditions.  

QuantistryLab enables the user to create and study mixtures of multiple chemicals using the workflow described in this use case, opening up a host of possibilities in lubricant R&D applications.  

This same approach can be extended to any number of applications across multiple areas of interest, including energy storage, metals and alloys, lubricants, semiconductors and specialty chemicals. For all these applications and more, QuantistryLab offers a full range of computational solutions to study the properties of a wide range of chemical and material systems.

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