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Gözdenur Toraman

Publications and source records attributed to Gözdenur Toraman.

2 recordsLinked to original sources

Reliable Viscosity Calculation from High-Pressure Equilibrium Molecular Dynamics: Case Study of 2,2,4-Trimethylhexane

Viscosity is a fundamental property of liquid lubricants, yet it is challenging to determine accurately, especially at high pressures. Although equilibrium molecular dynamics (EMD) simulations are a promising alternative to resource-intensive experiments, practical challenges remain in assessing the sufficiency of simulation time and in controlling uncertainties in the Green-Kubo formalism due to the finite amount of trajectory data. In this work, we extend the STable AutoCorrelation Integral Estimator (STACIE), a recently developed algorithm for estimating transport properties. First, we introduce the Lorentz model to estimate the viscosity and the exponential correlation time from the low-frequency power spectrum of deviatoric pressure fluctuations. Second, we show how to supplement the three conventional off-diagonal elements of the pressure tensor ($P_{xy}$, $P_{yz}$ and $P_{zx}$) with two additional uncorrelated deviatoric pressure components for shear viscosity calculations. Using these improvements, we apply STACIE to calculate the shear viscosity of 2,2,4-trimethylhexane from EMD simulations. We demonstrate STACIE's capability to reliably calculate viscosity under high-pressure conditions, offering a robust and automated solution with validated uncertainty quantification. Our results, when compared to the outcomes of the 10th International Fluid Properties Simulation Challenge, underscore the need for long EMD simulations. Large deviations from experimental viscosities in previous works were primarily due to insufficient simulation times and ad hoc post-processing choices, rather than the limitations of the force fields used. Unlike previous studies, our viscosity estimates agree well with experimental results (relative error < 6%) up to the highest pressure of 1 GPa, highlighting the improved reliability and accuracy of STACIE's systematic approach to viscosity predictions.

physics.comp-ph↗

STable AutoCorrelation Integral Estimator (STACIE): Robust and accurate transport properties from molecular dynamics simulations

STACIE (STable AutoCorrelation Integral Estimator) is a novel algorithm and Python package that delivers robust, uncertainty-aware estimates of autocorrelation integrals from time-correlated data. While its primary application is deriving transport properties from equilibrium molecular dynamics simulations, STACIE is equally applicable to time-correlated data in other scientific fields. A key feature of STACIE is its ability to provide robust and accurate estimates without requiring manual adjustment of hyperparameters. Additionally, one can follow a simple protocol to prepare sufficient simulation data to achieve a desired relative error of the transport property. We demonstrate its application by estimating the ionic electrical conductivity of a NaCl-water electrolyte solution. We also present a massive synthetic benchmark dataset to rigorously validate STACIE, comprising 15360 sets of time-correlated inputs generated with diverse covariance kernels with known autocorrelation integrals. STACIE is open source and available on GitHub and PyPI, with comprehensive documentation and examples.

physics.comp-ph↗