Spectral Recognition of Magnetic Nanoparticles with Artificial Neural Networks

TL;DR

This paper demonstrates that artificial neural networks can accurately identify internal magnetic anisotropy fields from FMR spectra, enabling faster and automated analysis of magnetic nanoparticles for biomedical and storage applications.

Contribution

The study introduces neural network models for inverse spectral analysis of magnetic nanoparticles, a novel approach that outperforms traditional methods and generalizes beyond training data.

Findings

Neural networks accurately predict anisotropy fields from FMR spectra.

Models perform well on data outside their training range.

Potential for high-throughput magnetic material analysis.

Abstract

Ferromagnetic resonance (FMR) spectroscopy is a powerful method for quantifying internal magnetic anisotropy fields in nanoparticles, which is important in a wide range of biomedical and storage applications. The interpretation of FMR spectra, however, can only be achieved with the use of an appropriate model, and no inverse methods are available to extract internal fields from FMR spectra. Here, we present the use of artificial neural networks for spectral recognition, i.e., to identify the internal magnetic anisotropy field from the FMR spectrum. We trained two different types of networks, a convolutional neural network and a multi-layer perceptron, by feeding the networks pre-computed FMR spectra labeled with the corresponding anisotropy fields. Testing of the trained networks with unseen spectra showed that they successfully predict the correct anisotropy fields and, surprisingly, the networks performed well for data that was beyond their training range. These results show the promise of using artificial neural networks for accelerated high-throughput analysis of magnetic materials and nanostructures; for example they could serve in automatizing and optimizing exploration missions where nanomagnetic signals are often used as proxies.