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Machine-learning interatomic potential for AlN for epitaxial simulation
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AI
DOI：10.1016/j.commatsci.2025.114473.png)
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Abstract
En 中文
• A machine-learned interatomic potential for AlN was developed with the ultra-fast force field (UF3) framework, enabling efficient large-scale atomistic simulations. • The potential achieves strong agreement with density functional theory in predicting key structural, mechanical, and surface properties across wurtzite, rock-salt, and zinc-blende AlN polytypes. • The potential accurately reproduces the experimentally observed atomic core structure of edge dislocations and the wurtzite crystal structure of the overlayer during AlN homoepitaxy, behavior that prior potentials failed to capture. • The potential combines accuracy with high computational efficiency, making predictive simulation of defect formation and evolution during AlN epitaxial growth feasible.
Journal
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3.3
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1.3W
Citations:
3.6W