Computing the heat conductivity of fluids from density fluctuations

Abstract

Equilibrium molecular dynamics simulations, in combination with the Green-Kubo (GK) method, have been extensively used to compute the thermal conductivity of liquids. However, the GK method relies on an ambiguous definition of the microscopic heat flux, which depends on how one chooses to distribute energies over atoms. This ambiguity makes it problematic to employ the GK method for systems with non-pairwise interactions. In this work, we show that the hydrodynamic description of thermally driven density fluctuations can be used to obtain the thermal conductivity of a bulk fluid unambiguously, thereby bypassing the need to define the heat flux. We verify that, for a model fluid with only pairwise interactions, our method yields estimates of thermal conductivity consistent with the GK approach. We apply our approach to compute the thermal conductivity of a non-pairwise additive water model at supercritical conditions, and then of a liquid hydrogen system described by a machine-learning interatomic potential, at 33 GPa and 2000 K.

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• DL_POLY 5: Calculation of system properties on the fly for very large systems via massive parallelism
• Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids