Rapid Determination of Nanodiamond Size Distribution and Impurity Concentration from Raman Spectra Using an Open Machine-Learning Toolbox

TL;DR

This paper introduces an open-source machine learning toolbox for rapid analysis of nanodiamond Raman spectra, enabling accurate size distribution and impurity concentration determination through neural networks and stochastic algorithms.

Contribution

It presents a novel computational approach combining neural networks and stochastic algorithms for nanodiamond property reconstruction from Raman spectra.

Findings

The toolbox accurately reproduces experimental Raman spectra.

Both methods show faithful agreement with experimental data.

The software is openly accessible and easy to use.

Abstract

Ready-to-use numerical toolbox for nanodiamond Raman spectra calculation and fit is presented. The developed theoretical approach allows accounting for arbitrary nanoparticle size-distribution and the microscopic line broadening mechanisms for the optical phonons. The two tools for solving the inverse problem of the nanodiamond properties reconstruction using a known Raman spectrum are provided. The first one utilizes a dense neural network trained on a vast array of synthetic Raman spectra. The second approach is based on the stochastic Metropolis algorithm, which updates the ensemble parameters by small quantities, tending to the state with minimal error. Both methods are available thanks to the computationally instant elasticity theory-like model for optical phonon modes in diamond nanocrystals that accurately reproduces the results of the atomistic approaches. Using experimental Raman spectra for nanodiamonds prepared by various techniques, we tested our tools and observed a faithful agreement with the data as well as between the two methods. The open and documented software is accessible online (nanoraman.pythonanywhere.com) and as a Python module (github.com/KoniakhinSV/Nanoparticle_Raman).