Deep Learning Inter-atomic Potential for Thermal and Phonon Behaviour of Silicon Carbide with Quantum Accuracy
arXiv:2110.10843
Abstract
Silicon carbide (SiC) is an essential material for next generation semiconductors and components for nuclear plants. It's applications are strongly dependent on its thermal conductivity, which is highly sensitive to microstructures. Molecular dynamics (MD) simulation is the most used methods to address thermal transportation mechanisms in devices or microstructures of nano-meters. However, the implementation of MD is limited in SiC because of lacking accurate inter-atomic potentials. In this work, using the Deep Potential (DP) methodology, we developed two inter-atomic potentials (DP-IAPs) for SiC based on two adaptively generated datasets within the density functional approximations at the local density and the generalized gradient levels. These two DP-IAPs manifest their speed with quantum accuracy in lattice dynamics simulations as well as scattering rate analysis of phonon transportation. Combining with molecular dynamics simulations, the thermal transport and mechanical properties were systematically investigated. The presented methodology and the inter-atomic potentials pave the way for a systematic approach to model heat transport in SiC related devices using multiscale modelling.
References in corpus (16)
- Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
- Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
- DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
- Distribution of phonon lifetime in Brillouin zone
- Machine Learning of Accurate Energy-Conserving Molecular Force Fields
- DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models
- Machine learning a general purpose interatomic potential for silicon
- Environment Dependent Interatomic Potential for Bulk Silicon
- Neural Network Based in Silico Simulation of Combustion Reactions
- A Machine Learning Potential for Graphene
- An enhanced version of the heat exchange algorithm with excellent energy conservation properties
- Silicon liquid structure and crystal nucleation from ab-initio deep Metadynamics
- Total energy differences between SiC polytypes revisited
- Pressure Dependence of Born Effective Charges, Dielectric Constant and Lattice Dynamics in SiC
- Temperature Effect on Phonon Dispersion Stability of Zirconium by Machine Learning-driven Atomistic Simulations
- Development of Interatomic Potential for Al-Tb Alloy by Deep Neural Network Learning Method