  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.
