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Preprint! "Inverse Design of Complex Nanoparticle Heterostructures via Deep Learning on Heterogeneous Graphs" is now on ChemRxiv. In this work, we consider the problem of applying deep learning to heterogeneous nanostructures. (1/10) #ChemSky 🧪 #CompChem #physics #optics #nanoscience

Former LBNL postdoc Dr. Eric Sivonxay (@samblau.bsky.social group) led this work that began with a simple goal: designing core-shell upconverting nanoparticles (UCNPs) that can convert low-energy near-infrared light to visible and ultraviolet emissions with high brightness. (2/10)

With SUNSET, we can learn structure-property relationships for these complex nanostructures. We considered a number of different representations for nanoparticles - simple feature matrices, voxels (for convolutional neural networks), and graphs (for graph neural networks or GNNs). (4/10)

We also designed a new heterogeneous graph representation, with one set of nodes representing UCNP dopants and another set representing interactions within a layer or between layers. We found that our hetero-GNN outperformed other models in-distribution and especially out-of-distribution. (5/10)

While testing out these different models, we realized that UCNPs and similar core-shell particles display a unique "subdivision invariance". You can define layers arbitrarily, and the particle behavior doesn't change, but the representations (including for heterographs) do change. (6/10)