In a particle utilizing all of these strategies, we find a 6.5x increase in I vis-U V , as compared to the brightest nanoparticle in the training set. Even within the feature distribution, the optimization identifies a particle which utilizes these design rules to achieve a 2x increase in emission intensity as compared to the brightest particle in the training set. These results illustrate that, even within a training distibution but especially far OOD, optimization with a differentiable hetero-GNN can rapidly discover new structures with properties that exceed historical training data and identify novel heterostructure design rules.