posted on 2021-08-26, 22:29authored byKatsuya Shiratori, Logan D. C. Bishop, Behnaz Ostovar, Rashad Baiyasi, Yi-Yu Cai, Peter J. Rossky, Christy F. Landes, Stephan Link
Electron
microscopy is often required to correlate the size and
shape of plasmonic nanoparticles with their optical properties. Eliminating
the need for electron microscopy is one crucial step toward in situ sensing applications, especially for complicated
sample conditions such as during irreversible chemical reactions or
when particles are embedded in a matrix. Here, we show that a machine
learning decision tree can accurately predict gold nanorod dimensions
over a wide range of sizes. The model is trained by using ∼450
nanorod geometries and corresponding scattering spectra obtained from
finite-difference time-domain simulations. We test the model using
a set of experimental spectra and sizes obtained from correlated scanning
electron microscopy images, resulting in predictions of the dimensions
of gold nanorods within ∼10% of their true values (root-mean-squared
percentage error) over a large range of sizes. Analysis of the decision
tree structure reveals that a relationship with resonance energy and
line width of the localized surface plasmon resonance is sufficient
to predict nanorod dimensions, notably outperforming more complicated
models. Our findings illustrate the advantages of using machine learning
models to infer single particle structural features from their optical
spectra.