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Updated: Jan 24, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
Published on: November 11, 2022
Plasmonic colours predicted by deep learning
Joshua Baxter1,2, Antonino Calà Lesina3,4,5, Jean-Michel Guay6,7,8
1University of Ottawa, Department of Physics, Ottawa, K1N 6N5, Canada. jbaxt089@uottawa.ca.
Abstract:
Picosecond laser pulses have been used as a surface colouring technique for noble metals, where the colours result from plasmonic resonances in the metallic nanoparticles created and redeposited on the surface by ablation and deposition processes. This technology provides two datasets which we use to train artificial neural networks, data from the experiment itself (laser parameters vs. colours) and data from the corresponding numerical simulations (geometric parameters vs. colours). We apply deep learning to predict the colour in both cases. We also propose a method for the solution of the inverse problem - wherein the geometric parameters and the laser parameters are predicted from colour - using an iterative multivariable inverse design method.
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