  Undesirable natural aging (NA) in Al-6xxx delays subsequent artificial aging
(AA) but the size, composition, and evolution of clustering are challenging to
measure. Here, atomistic details of early-stage clustering in Al-1\%Mg-0.6\%Si
during NA are studied computationally using a chemically-accurate
neural-network potential. Feasible growth paths for the preferred $\beta''$
precipitates identify: dominant clusters differing from $\beta''$ motifs;
spontaneous vacancy-interstitial formation creating 14-18 solute atom
$\beta''$-like motifs; and lower-energy clusters requiring chemical
re-arrangement to form $\beta''$ nuclei. Quasi-on-lattice kinetic Monte Carlo
simulations reveal that 8-14 solute atom clusters form within 1000 s but that
growth slows considerably due to vacancy trapping inside clusters, with
trapping energies of 0.3-0.5 eV. These findings rationalize why cluster growth
and alloy hardness saturate during NA, confirm the concept of ''vacancy
prisons", and suggest why clusters must be dissolved during AA before formation
of $\beta''$. This atomistic understanding of NA may enable design of
strategies to mitigate negative effects of NA.
