An Accurate and Efficient Machine-Learned Potential for SiC from Ambient to Extreme Environments

2025/10/02 by Wu, Jintong, Shao, Zhuang, Zhao, Junlei +5
#FOS: Physical sciences #Materials Science (cond-mat.mtrl-sci)

paper · doi:10.48550/arxiv.2510.01827

Abstract

Silicon carbide (SiC) polymorphs are widely employed as nuclear materials, mechanical components, and wide-bandgap semiconductors. The rapid advancement of SiC-based applications has been complemented by computational modeling studies, including both ab initio and classical atomistic approaches. In this work, we develop a computationally efficient and general-purpose machine-learned interatomic potential (ML-IAP) capable of multimillion-atom molecular dynamics simulations over microsecond timescales. Using the ML-IAP, we systematically map the comprehensive pressure-temperature phase diagram and the threshold displacement energy distributions for the 2H and 3C polymorphs. Furthermore, collision cascade simulations provide in-depth insights into polymorph-dependent primary radiation damage clustering, a phenomenon that conventional empirical potentials fail to accurately capture.

Citations

• SiC-TGAP: A machine learning interatomic potential for radiation damage simulations in 3C-SiC
• Incongruent Melting and Phase Diagram of SiC from Machine Learning Molecular Dynamics
• A Genetic Algorithm Trained Machine-Learned Interatomic Potential for the Silicon-Carbon System
• Deep learning inter-atomic potential for irradiation damage in 3C-SiC
• Massively Parallel Fitting of Gaussian Approximation Potentials
• Uncertainty-aware molecular dynamics from Bayesian active learning for Phase Transformations and Thermal Transport in SiC
• LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales
• Gaussian Process Regression for Materials and Molecules
• <i>Ab‐initio</i>simulations of materials using VASP: Density‐functional theory and beyond
• Displacement threshold energies in β-SiC

Related

• SiC-TGAP: A machine learning interatomic potential for radiation damage simulations in 3C-SiC
• Deep learning inter-atomic potential for irradiation damage in 3C-SiC
• Characterizing Defect Dynamics in Silicon Carbide Using Symmetry-Adapted Collective Variables and Machine Learning Interatomic Potentials
• A Genetic Algorithm Trained Machine-Learned Interatomic Potential for the Silicon-Carbon System
• Deep Learning Inter-atomic Potential for Thermal and Phonon Behaviour of Silicon Carbide with Quantum Accuracy