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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)

Towards this end, we performed thousands of high-fidelity kinetic Monte Carlo simulations (using RNMC, recently published in JOSS) and compiled them into a dataset called SUNSET - Simulated Upconverting Nanoparticle Spectra for Emission Tuning. (3/10)

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)

26/12/24, 8:28 pm0 reposts2 likes

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)

To address this, we included on-the-fly data augmentation, where the same particle would be included in a training batch with multiple layers divided in multiple different ways. This data augmentation allowed us to effectively learn subdivision invariance and lower our OOD error. (7/10)