Computational prediction of lattice thermal conductivity -- a comparison of molecular dynamics and Boltzmann transport approaches

Abstract

The predictive modeling of lattice thermal conductivity is of fundamental importance for the understanding and design of materials for a wide range of applications. Two major approaches, namely molecular dynamics (MD) simulations and calculations solving approximately the Boltzmann transport equation (BTE), have been developed to compute the lattice thermal conductivity. We present a detailed direct comparison of these two approaches, using as prototypical cases MgO and PbTe. The comparison, carried out using empirical potentials, takes into account the effects of fourth order phonon scattering, temperature-dependent phonon frequencies (phonon renormalization), and investigates the effects of quantum vs. classical statistics. We clarify that equipartition, as opposed to Maxwell Boltzmann, govern the statistics of phonons in MD simulations. We find that lattice thermal conductivity values from MD and BTE show an apparent, satisfactory agreement; however such an agreement is the result of error cancellations. We also show that the primary effect of statistics on thermal conductivity is via the scattering rate dependence on phonon populations.

References in corpus (13)

• Lattice dynamics of anharmonic solids from first principles
• Self-consistent phonon calculations of lattice dynamical properties in cubic SrTiO${}_3$ with first-principles anharmonic force constants
• Anharmonic free energies and phonon dispersions from the stochastic self-consistent harmonic approximation: application to platinum and palladium hydrides
• Quantum mechanical prediction of four-phonon scattering rates and reduced thermal conductivity of solids
• Lattice anharmonicity and thermal conductivity from compressive sensing of first-principles calculations
• Compressive sensing as a new paradigm for model building
• Ab initio Green-Kubo Approach for the Thermal Conductivity of Solids
• Microscopic theory and quantum simulation of atomic heat transport
• Cluster expansion made easy with Bayesian compressive sensing
• Anharmonic stabilization of the high-pressure simple cubic phase of calcium
• Anomalous thermal conductivity and suppression of negative thermal expansion in ScF3
• Accurate thermal conductivities from optimally short molecular dynamics simulations
• Anharmonic stabilization and lattice heat transport in rocksalt $\beta$-GeTe

Cited by in corpus (14)

• Anharmonicity Measure for Materials
• Accurate prediction of heat conductivity of water by a neuroevolution potential
• Transferability of neural network potentials for varying stoichiometry: phonons and thermal conductivity of Mn${}_x$Ge${}_y$ compounds
• Temperature- and vacancy-concentration-dependence of heat transport in Li${}_3$ClO from multi-method numerical simulations
• Ab initio Green-Kubo simulations of heat transport in solids: Method and implementation
• Heat transport in carbon nanotubes: Length dependence of phononic conductivity from the Boltzmann transport equation and molecular dynamics
• Thermal conductivity of Li${}_3$PS${}_4$ solid electrolytes with ab initio accuracy
• Nontrivial impact of interlayer coupling on thermal conductivity: opposing trends in in-plane and out-of-plane phonons
• Machine Learning and First-Principles Predictions of Materials with Low Lattice Thermal Conductivity
• Understanding Phonon Transport Properties Using Classical Molecular Dynamics Simulations
• Consistency between the Green-Kubo formula and Lorentz model for predicting the infrared dielectric function of polar materials
• Thermal Conductivity Modeling using Machine Learning Potentials: Application to Crystalline and Amorphous Silicon
• Predicting the Thermal Conductivity Collapse in SWCNT Bundles: The Interplay of Symmetry Breaking and Scattering Revealed by Machine-Learning-Driven Quantum Transport
• Thermal transport and phase transitions of zirconia by on-the-fly machine-learned interatomic potentials