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  3. Characterizing Defect Dynamics In Silicon Carbide Using Symmetry-adapted Collective Variables And Machine Learning Interatomic Potentials.
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  3. Characterizing Defect Dynamics In Silicon Carbide Using Symmetry-adapted Collective Variables And Machine Learning Interatomic Potentials.

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

Soumajit Dutta1, Cunzhi Zhang1, Gustavo Perez Lemus1

  • 1Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States.

Journal of Chemical Theory and Computation
|April 23, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Machine learning potentials accelerate simulations of silicon carbide (SiC) divacancies, crucial for quantum computing qubits. This method accurately predicts defect behavior, enabling optimized material processing for enhanced qubit stability.

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Area of Science:

  • Materials Science
  • Quantum Computing
  • Computational Physics

Background:

  • Silicon carbide (SiC) divacancies are promising for spin-defect qubits due to long coherence times and optical addressability.
  • Simulating SiC defect dynamics is computationally intensive due to high activation barriers, limiting first-principles molecular dynamics studies.

Purpose of the Study:

  • To develop and implement machine learning interatomic potentials (MLIPs) for accelerated and accurate SiC defect dynamics simulations.
  • To retain first-principles accuracy in simulations of defect formation, motion, and thermodynamic stability.

Main Methods:

  • An active learning strategy was employed, combining symmetry-adapted collective variable discovery and enhanced sampling.
  • Density functional theory (DFT) was used for calculating energies and forces to train an E(3)-equivariant MLIP (Allegro model).
  • Simulations were performed on multidefect 216-atom SiC supercells.

    • Main Results:

    • The trained MLIP achieved DFT-level accuracy for defect transition activation free energy barriers.
    • Efficient and stable simulations of large supercells were enabled, allowing analysis of defect thermodynamics and kinetics.
    • The temperature dependence of defect stability and kinetics was analyzed.

    Conclusions:

    • MLIPs significantly accelerate SiC defect dynamics simulations while maintaining high accuracy.
    • The study proposes an optimal annealing temperature to maximize the stabilization of VV divacancies for qubit applications.
    • This approach facilitates the study of complex defect phenomena in materials for quantum technologies.