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06 October 2024 |
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Article overview
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Spectral Recognition of Magnetic Nanoparticles with Artificial Neural Networks | | David Slay
; Michalis Charilaou
; | | Date: |
1 Jun 2022 | | 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. | | Source: | arXiv, 2206.00166 | | Services: | Forum | Review | PDF | Favorites | |
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