Atomistic Mechanism Underlying the Si(111)-(7\times7) Surface Reconstruction Revealed by Artificial Neural-network Potential

Abstract

The 7\times7 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\times7) 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\times7) surface. We reveal a possible step-mediated atom-pop rate-limiting process that triggers massive non-conserved 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\times7 reconstruction. Our findings may not only solve the long-standing mystery of this famous surface reconstruction but also illustrate the power of machine learning in studying complex structures.

References in corpus (8)

• Machine learning a general purpose interatomic potential for silicon
• Efficient and Accurate Machine-Learning Interpolation of Atomic Energies in Compositions with Many Species
• Accurate Interatomic Force Fields via Machine Learning with Covariant Kernels
• Computing Gibbs free energy differences by interface pinning
• Melting Si: beyond density functional theory
• Electron correlation and the phase diagram of Si
• Machine Learning for Atomic Forces in a Crystalline Solid: Transferability to Various Temperatures
• Size and Temperature Transferability of Direct and Local Deep Neural Networks for Atomic Forces

Cited by in corpus (3)

• Advances in modeling complex materials: The rise of neuroevolution potentials
• Complex Spin Hamiltonian Represented by Artificial Neural Network
• Intermediates of Forming Transition Metal Dichalcogenide Heterostructures Revealed by Machine Learning Simulations