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Evan Spotte-Smith (they/them)
@ewcss.info

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

doi.orgInverse Design of Complex Nanoparticle Heterostructures via Deep Learning on Heterogeneous GraphsApplications of deep learning (DL) to design nanomaterials are hampered by a lack of suitable data representations and training data. We report efforts to overcome these limitations and leverage DL to...

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Evan Spotte-Smith (they/them)
@ewcss.info

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)

Evan Spotte-Smith (they/them)
@ewcss.info

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)

Evan Spotte-Smith (they/them)
@ewcss.info

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)