标题： 基于人工神经网络的磁性纳米粒子光谱识别 显示英文标题

标题： Spectral Recognition of Magnetic Nanoparticles with Artificial Neural Networks

摘要： 铁磁共振（FMR）光谱学是一种用于量化纳米颗粒内部磁各向异性场的强大方法，这在生物医学和存储应用的广泛领域中非常重要。 然而，FMR光谱的解释只能通过使用适当的模型来实现，目前没有逆方法可以从FMR光谱中提取内部场。 在这里，我们介绍了人工神经网络在光谱识别中的应用，即从FMR光谱中识别内部磁各向异性场。 我们通过将预计算的带有相应各向异性场标签的FMR光谱输入网络，训练了两种不同类型的网络，卷积神经网络和多层感知器。 用未见过的光谱测试训练好的网络显示，它们能够成功预测正确的各向异性场，并且令人惊讶的是，这些网络在超出其训练范围的数据上表现良好。 这些结果表明，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具有前景；例如，它们可用于自动化和优化探索任务，其中纳米磁信号经常被用作代理。 显示英文摘要

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.