Direct calculation of modal contributions to thermal conductivity via Green–Kubo modal analysis

By Wei-Ling Lv, A. Henry. 2015

152 citations recorded by the source corpus.

We derived a new method for direct calculation of the modal contributions to thermal conductivity, which is termed Green–Kubo modal analysis (GKMA). The GKMA method combines the lattice dynamics formalism with the Green–Kubo formula for thermal conductivity, such that the thermal conductivity becomes a direct summation of modal contributions, where one need not define the phonon velocity. As a result, the GKMA method can be applied to any material/group of atoms, where the atoms vibrate around stable equilibrium positions, which includes non-stoichiometric compounds, random alloys, amorphous materials and even rigid molecules. By using molecular dynamics simulations to obtain the time history of each mode’s contribution to the heat current, one naturally includes anharmonicity to full order and can obtain insight into the interactions between different modes through the cross-correlations. As an example, we applied the GMKA method to crystalline and amorphous silicon. The modal contributions at each frequency result from the analysis and thereby allow one to apply a quantum correction to the mode heat capacity to determine the temperature dependence of thermal conductivity. The predicted temperature dependent thermal conductivity for amorphous silicon shows the best agreement with experiments to date. The GKMA method provides new insight into the nature of phonon transport, as it casts the problem in terms of mode–mode correlation instead of scattering, and provides a general unified formalism that can be used to understand phonon–phonon interactions in essentially any class of materials or structures where the atoms vibrate around stable equilibrium sites.

Subfields: Civil and Structural Engineering, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/27ce89982bc03310cdb0be2f23d85a01c715825a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Materials Chemistry

1. T. Ala‐Nissila
2. Haikuan Dong
3. Zezhu Zeng
4. Zheyong Fan
5. Cunzhi Zhang
6. Yue Chen
7. Yanzhou Wang
8. Keke Song

Citations in the corpus

• Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport (2021, methodology): “…no universally applicable quantum correction method for classical MD simulations of phonon thermal transport in perfect crystals, a feasible one [94–96] for disordered materials is to multiply a factor to the classical spectral thermal conductivity κ µν ( ω ) to obtain the following…”