The strength of aluminum alloys, the most widely used lightweight structural materials, mainly originates from coherent complex-structured nanoplates. Yet, the atomic-scale mechanisms governing their formation remain elusive, owing to the inherent difficulty of probing and simulating solid-state nucleation and growth events. By combining density functional theory (DFT) calculations and machine learning molecular dynamics (ML-MD) simulations, we reveal a unified “interlayer-sliding+shuffling” mechanism for three technologically significant complex-structured nanoplates—${\mathrm{T}}_1$, Ω, and ${\eta }^{\prime }/{\eta }_2$—in commercial Al alloys. Despite their distinct crystal structures, all three form via the same sequence: partial dislocations first drive the interlayer sliding to create a local hexagonal close-packed (hcp) environment, after which an unstable solute cluster, termed the basic structural transformation unit (BSTU), triggers atomic shuffling into the final complex phase. Identifying distinct BSTUs for each nanoplate enables reconstruction of their structural evolution pathways, reproducing experimental thickness distributions and enabling critical-nucleus assessment. Furthermore, we show that solute-enriched GP zones reduce stacking-fault energy and stabilize partial dislocations, while diffusional glide driven by local solute fluctuations controls their migration. These findings offer a unified physical picture of complex nanoprecipitation in aluminum alloys and may provide a useful framework for understanding $\mathrm{fcc}\to \mathrm{complex}$ transformations in related metallic systems.

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• Nucleation
• Phase transitions
• Solid-solid transformations
• Density functional calculations
• Machine learning