Thiolate-protected gold nanoclusters occupy a unique domain between molecular complexes and metallic nanoparticles, exhibiting size-specific electronic structure, ligand-mediated stability, and emergent collective behavior during synthesis. This work introduces and validates a machine learning-driven strategy to predict atomistic coalescence mechanisms of self-assembly of gold-thiolate nanoclusters across a significant size range of products (A36-Au851) and thermal conditions (500-700 K). We used an atomic cluster expansion interatomic potential trained on density functional theory data, enabling molecular dynamics simulations up to 0.1 µ timescale. By systematically simulating both homo- and heterocoalescence reactions, we identify a hierarchical reaction network in which ligand dynamics, transient metal exposure, and geometric compatibility jointly govern selection of fusion pathways. Generating and analyzing 87 distinct coalescence products, we identify size-dependent fusion behaviors and ligand-gated reactive windows that govern hierarchical cluster growth. Quantitative comparison with experimentally identified metal-ligand stoichiometries validates that our simulations reproduce experimentally observed surface-to-volume scaling laws and ligand coverage trends spanning more than a 20-fold range of cluster sizes. The robustness of these predictions is demonstrated through statistical analysis of reaction trajectories and structural motif evolution. Our strategy can be generalized to other ligand-protected metal nanoclusters and nanoparticle systems, provided sufficient training data is generated, offering a predictive framework for rational design of size-focused synthesis, controlled aggregation, and hierarchical assembly in nanocluster-based materials.
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