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Meeting 2025 TMS Annual Meeting & Exhibition
Symposium Bridging Scale Gaps in Multiscale Materials Modeling in the Age of Artificial Intelligence
Presentation Title Pathways to the 7 × 7 Surface Reconstruction of Si(111) Revealed by Machine-Learning Molecular Dynamics Simulations
Author(s) Yidi Shen, Kun Luo, William A Goddard, Qi An
On-Site Speaker (Planned) Qi An
Abstract Scope The well-known 7 × 7 structure of the Si(111) surface, formed upon annealing, has long puzzled researchers due to its complex reconstruction mechanism. We employ molecular dynamics simulations enhanced by a machine-learning force field for silicon to explore the formation of the Si(111)-7 × 7 surface reconstruction from the melt. Our simulations reveal two potential pathways: the growth of a faulted half domain transitioning from a metastable 5 × 5 phase to the final 7 × 7 configuration, and the direct formation of the 7 × 7 structure. Both pathways involve the creation of dimers and bridged five-membered rings, followed by the stabilization of triangular halves within the unit cell. The distinctive corner hole forms through the aggregation of multiple five-member rings. Additionally, atom insertion beneath adatoms to establish a dumbbell configuration requires extra diffusion or rearrangement. Our results provide valuable insights into surface reconstruction in semiconductors.
Proceedings Inclusion? Planned:
Keywords Machine Learning, Surface Modification and Coatings, Thin Films and Interfaces

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Peierls-Nabarro Modeling of Dislocations in High Entropy Alloys
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