First, state-of-the-art approaches for representing materials often fail to capture the structural complexity of nanomaterials, such as multi-shell nanoparticles (Figure ) and nanowire heterojunctions, in which nanostructure controls energy transport (Figure ). Nanomaterials exhibit distinct and often superior properties compared to their bulk counterparts, driven by nanoscale confinement and high surface areas relative to their volume. However, the large number of features required to adequately describe a nanoscale material (e.g., the morphology, dimensions, composition, heterogeneity, doping, defects in each domain; internal interfaces, surface ligands) make training on naive tabular representations computationally inefficient, in part because they neglect physical relationships between features. While bulk crystals can be represented by their unit cell coordinates (e.g., CIF) and small organic molecules by strings (SMILES, SELFIES, ), graphs, or atomic coordinates, such atomistic representations are impractical for complex nanomaterials because their critical features often span length scales of one to >10 6 atoms and cannot necessarily be reduced to periodic subunits. More recent DL approaches encode spatial information as pixels or voxels, but these fixed-resolution representations cannot efficiently capture the structural hierarchy of a wide range of nanomaterials, e.g., those of different sizes.