Atomistic Mechanism Underlying the Si(111)(7×7) Surface Reconstruction Revealed by Artificial Neural-Network Potential

Lin Hu, Bing Huang, and Feng Liu
Phys. Rev. Lett. 126, 176101 – Published 30 April 2021
PDFHTMLExport Citation

Abstract

The 7×7 reconstruction of the Si(111) surface represents arguably the most fascinating surface reconstruction so far observed in nature. Yet, the atomistic mechanism underpinning its formation remains unclear after it was discovered sixty years ago. Experimentally, it is observed post priori so that analysis of its formation mechanism can only be carried out in analogy with archaeology. Theoretically, density-functional theory (DFT) correctly predicts the Si(111)(7×7) ground state but is impractical to simulate its formation process; while empirical potentials failed to produce it as the ground state. Developing an artificial neural-network potential of DFT quality, we carried out accurate large-scale simulations to unravel the formation of the Si(111)(7×7) surface. We reveal a possible step-mediated atom-pop rate-limiting process that triggers massive nonconserved atomic rearrangements, most remarkably, a critical process of collective vacancy diffusion that mediates a sequence of selective dimer, corner-hole, stacking-fault, and dimer-line pattern formation, to fulfill the 7×7 reconstruction. Our findings may not only solve the long-standing mystery of this famous surface reconstruction but they also illustrate the power of machine learning in studying complex structures.

  • Figure
  • Figure
  • Figure
  • Figure
  • Received 7 June 2020
  • Revised 9 October 2020
  • Accepted 31 March 2021

DOI:https://doi.org/10.1103/PhysRevLett.126.176101

© 2021 American Physical Society

Physics Subject Headings (PhySH)

Condensed Matter, Materials & Applied Physics

Authors & Affiliations

Lin Hu1, Bing Huang1,*, and Feng Liu2,†

  • 1Beijing Computational Science Research Center, Beijing 100193, China
  • 2Department of Materials Science and Engineering, University of Utah, Salt Lake City, Utah 84112, USA

  • *Corresponding author. bing.huang@csrc.ac.cn
  • Corresponding author. fliu@eng.utah.edu

Article Text (Subscription Required)

Click to Expand

Supplemental Material (Subscription Required)

Click to Expand

References (Subscription Required)

Click to Expand
Issue

Vol. 126, Iss. 17 — 30 April 2021

Reuse & Permissions
Access Options
CHORUS

Article Available via CHORUS

Download Accepted Manuscript
Author publication services for translation and copyediting assistance advertisement

Authorization Required


×
×

Images

×

Sign up to receive regular email alerts from Physical Review Letters

Log In

Cancel
×

Search


Article Lookup

Paste a citation or DOI

Enter a citation
×