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Comparing Machine Learning and Physics-Based Nanoparticle Geometry Determinations Using Far-Field Spectral Properties
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DOI：10.1021/acs.jpcc.5c07879.png)
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Abstract
En 中文
Anisotropic metal nanostructures exhibit polarization-dependent light scattering. This property has been widely exploited to determine the geometries of subwavelength structures using far-field microscopy. Here, we explore the use of variational autoencoders (VAEs) to determine the geometries of gold nanorods (NRs) such as in-plane orientation and aspect ratio under linearly polarized dark-field illumination in an optical microscope. We input polarized dark-field scattering spectra and electron microscopy images into a dual-branch multimodal VAE with a single shared latent space trained on paired spectra-image data using a learnable linear adapter. We achieve prediction of Au NRs using only polarized dark-field scattering spectra input. We determine geometrical parameters of orientational angle and aspect ratio quantitatively via both dual-VAE and physics-based analysis. We show that orientational angle prediction by dual-VAE performs well with only a small (similar to 300 particle) training set, yielding a mean absolute error (MAE) of 14.4 degrees and a concordance correlation coefficient (CCC) of 0.95. This performance is only marginally worse than the physics-based cos(2 theta) fitting approach between the scattering intensity and the polarizing angle, which achieves an MAE of 8.78 degrees and a CCC of 0.99. Aspect ratio determination is also similar for the dual-VAE and physics-based fitting comparison (MAE of 0.21 vs 0.23 and a CCC of 0.53 vs 0.68). We show that the spectra-to-structure inference route of dual-VAE achieves high reconstruction accuracy when referenced against well-established physics approach and effectively handles images and spectral data sets, suggesting a general recipe for inverse nano-optical problems requiring both structure and optical information.
Keywords:
GOLD NANORODS
2-PHOTON LUMINESCENCE
ORIENTATION
SERS
VALIDATION
EVALUATE
TRACKING
SENSORS
DESIGN
GROWTH
Journal
IF:
3.2
Papers:
5.6W
Citations:
15.0W