Characterizing Defect Dynamics in Silicon Carbide Using Symmetry-Adapted Collective Variables and Machine Learning Interatomic Potentials

TL;DR

This paper introduces a machine learning interatomic potential trained with active learning and enhanced sampling to accurately simulate defect dynamics in silicon carbide, overcoming computational challenges of first-principles methods.

Contribution

It develops a symmetry-adapted, E(3)-equivariant MLIP that reproduces DFT accuracy, enabling efficient large-scale defect simulations in SiC.

Findings

MLIP accurately reproduces DFT activation barriers

Enables stable simulation of 216-atom supercells

Identifies optimal annealing temperature for defect stability

Abstract

Silicon carbide (SiC) divacancies are attractive candidates for spin defect qubits possessing long coherence times and optical addressability. The high activation barriers associated with SiC defect formation and motion pose challenges for their study by first-principles molecular dynamics. In this work, we develop and deploy machine learning interatomic potentials (MLIPs) to accelerate defect dynamics simulations while retaining ab initio accuracy. We employ an active learning strategy comprising symmetry-adapted collective variable discovery and enhanced sampling to compile configurationally diverse training data, calculation of energies and forces using density functional theory (DFT), and training of an E(3)-equivariant MLIP based on the Allegro model. The trained MLIP reproduces DFT-level accuracy in defect transition activation free energy barriers, enables the efficient and stable simulation of multi-defect 216-atom supercells, and permits an analysis of the temperature dependence of defect thermodynamic stability and formation/annihilation kinetics to propose an optimal annealing temperature to maximally stabilize VV divacancies.