Revisit Many-body Interaction Heat Current and Thermal Conductivity Calculation in Moment Tensor Potential/LAMMPS Interface
arXiv:2411.01255 · doi:10.1021/acs.jctc.4c01659
Abstract
The definition of heat current operator for systems for non-pairwise additive interactions and its impact on related lattice thermal conductivity () via molecular dynamics simulation (MD) are ambiguous and controversial when migrating from conventional empirical potential models to machine learning potential (MLP) models. Empirical model descriptions are often limited to three- to four-body interaction while a sophisticated representation of the many-body physics could be resembled in MLPs. Herein, we study and compare the significance of many-body interaction to the heat current computation in one of the most popular MLP models, the Moment Tensor Potential (MTP). Non-equilibrium MD simulations and equilibrium MD simulations among four different materials, , amorphous , graphene, and , were performed. We found inconsistency between the simulation thermostat and its implemented heat current operator in our non-equilibrium MD results which violate law of energy conservation and suggest a need for revision. We revisit the virial stress tensor expression within the calculator and identified the lack of a generalised many-body heat current description in it. We uncover the influence of the modified heat current formula that could alter the results 29% to 64% using the equilibrium MD computational approach. Our work demonstrates the importance of a many-body description during thermal analysis in MD simulations when MLPs are in concern. This work sheds light on a better understanding of the relationship between interatomic interaction and its heat transport mechanism.
9 pages, 9 figures
References in corpus (9)
- A Performance and Cost Assessment of Machine Learning Interatomic Potentials
- Force and heat current formulas for many-body potentials in molecular dynamics simulation with applications to thermal conductivity calculations
- An Accurate and Transferable Machine Learning Potential for Carbon
- Accelerating first-principles estimation of thermal conductivity by machine-learning interatomic potentials: A MTP/ShengBTE solution
- Thermal conductivity decomposition in two-dimensional materials: Application to graphene
- Divergence of the Thermal Conductivity in Uniaxially Strained Graphene
- A revisit to phonon-phonon scattering in single-layer graphene
- Molecular dynamics simulations of heat transport using machine-learned potentials: A mini review and tutorial on GPUMD with neuroevolution potentials
- Heat flux for semi-local machine-learning potentials