A hybrid machine learning model to study UV-Vis spectra of gold nano spheres

TL;DR

This paper combines PCA, LDA, and neural networks to analyze UV-Vis spectra of gold nanospheres, enabling accurate size prediction and revealing physical phenomena like Fano resonances and quantum effects.

Contribution

It introduces hybrid machine learning algorithms integrating PCA and ANN for precise gold nanoparticle size estimation from spectral data.

Findings

PCA eigen spectra reveal Fano resonances.

LDA 3D spectra show quantum confinement effects.

Hybrid PCA-ANN models accurately predict nanoparticle sizes.

Abstract

Here, we have employed Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) to analyze Mie calculated UV-Vis spectra of gold nanospheres (GNS). Eigen spectra of PCA perform the Fano type resonances.3D vector field spectra reveal the Homoclinic orbit Lorenz attractor. Quantum confinement effects are observed by 3D representation of LDA. Standing wave patterns resulting from oscillations of ion acoustic phonon and electron waves are illustrated through the eigen spectra of LDA. Such capabilities of GNPs have brought high attention for the high energy density physics applications. Furthermore, accurate prediction of gold nanoparticle (GNP) sizes using machine learning could provide rapid analysis without the need for expensive analysis. Two hybrid algorithms consist of unsupervised PCA and two different supervised ANN have been used to estimate the diameters of GNPs. PCA based artificial neural network (ANN) were found to estimate the diameters with a high accuracy.