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

Dutta, Soumajit; Zhang, Cunzhi; Lemus, Gustavo Perez; de Pablo, Juan J.; Gygi, Francois; Galli, Giulia; Ferguson, Andrew L.

doi:10.1021/acs.jctc.6c00063

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

Journal Article · Thu Apr 23 00:00:00 UTC 2026 · Journal of Chemical Theory and Computation

DOI: https://doi.org/10.1021/acs.jctc.6c00063 · OSTI ID:3030657

^[1]; Zhang, Cunzhi ^[1]; Lemus, Gustavo Perez ^[1]; de Pablo, Juan J. ^[2];

^[3];

^[4];

^[1]

University of Chicago, IL (United States)
New York University, NY (United States)
University of California, Davis, CA (United States)
University of Chicago, IL (United States); Argonne National Laboratory (ANL), Argonne, IL (United States)

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. Here, the trained MLIP reproduces DFT-level accuracy in defect transition activation free energy barriers, enables the efficient and stable simulation of multidefect 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.

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Research Organization:: UChicago Argonne, LLC, Chicago, IL (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE Office of Science (SC), Basic Energy Sciences (BES). Materials Sciences & Engineering Division (MSE)

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 3030657

Journal Information:: Journal of Chemical Theory and Computation, Journal Name: Journal of Chemical Theory and Computation; ISSN 1549-9626; ISSN 1549-9618

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

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