人工神经网络用于磁性纳米粒子的光谱识别
Spectral Recognition of Magnetic Nanoparticles with Artificial Neural Networks
摘要：
铁磁共振（FMR）光谱是一种强有力的方法来量化内部磁各向异性场的纳米粒子，这是重要的，在广泛的生物医学和存储应用。然而，FMR谱的解释只能通过使用适当的模型来实现，并且没有逆方法可以从FMR谱中提取内部场。在这里，我们提出了使用人工神经网络进行光谱识别，即，从FMR谱中识别内部磁各向异性场。我们训练了两种不同类型的网络，一个卷积神经网络和一个多层感知器，通过给网络预先计算的FMR谱，用相应的各向异性场标记。对具有不可见光谱的训练网络的测试表明，它们成功地预测了正确的各向异性场，并且令人惊讶的是，网络在超出其训练范围的数据上表现良好。这些结果显示了使用人工神经网络加速磁性材料和纳米结构的高通量分析的前景;例如，它们可以用于自动化和优化勘探任务，其中纳米磁性信号通常用作代理。
英文摘要：
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.
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