Thermal Conductivity Calculation using Homogeneous Non-equilibrium Molecular Dynamics Simulation with Allegro
arXiv:2403.14130 · doi:10.1016/j.ijheatmasstransfer.2024.126106
Abstract
In this study, we derive the heat flux formula for the Allegro model, one of machine-learning interatomic potentials using the equivariant deep neural network, to calculate lattice thermal conductivity using the homogeneous non-equilibrium molecular dynamics (HNEMD) method based on the Green-Kubo formula. Allegro can construct more advanced atomic descriptors than conventional ones, and can be applied to multicomponent and large-scale systems, providing a significant advantage in estimating the thermal conductivity of anharmonic materials, such as thermoelectric materials. In addition, the spectral heat current (SHC) method, recently developed for the HNEMD framework (HNEMD-SHC), allows the calculation of not only the total thermal conductivity but also its frequency components. The verification of the heat flux and the demonstration of HNEMD-SHC method are performed for the extremely anharmonic low-temperature phase of AgSe.
References in corpus (7)
- E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
- MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields
- Heat transport in liquid water from first-principles and deep-neural-network simulations
- Mechanisms of temperature-dependent thermal transport in amorphous silica from machine-learning molecular dynamics
- Spin-Dependent Graph Neural Network Potential for Magnetic Materials
- Construction of Machine-Learning Interatomic Potential Under Heat Flux Regularization and Its Application to Power Spectrum Analysis for Silver Chalcogenides
- Atomic cluster expansion force field based thermal property material design with density functional theory level accuracy in non-equilibrium molecular dynamics calculations over sub-million atoms