Quantitative estimation of properties from core-loss spectrum via neural network
Quantitative estimation of properties from core-loss spectrum via neural network
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DOI：
10.1088/2515-7639/ab0b68
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发表时间：
2019-04-01
影响因子：
4.8
通讯作者：
Mizoguchi, Teruyasu
中科院分区：
文献类型：
--
作者：
Kiyohara, Shin;Tsubaki, Masashi;Mizoguchi, Teruyasu
纳米和亚纳米尺度的局部结构对材料性能有很大影响。因此，一些光谱技术已被用于表征局部原子和电子结构。如果材料的性质可以通过光谱观测直接“测量”，那么对材料性质的原子尺度的理解将会大大促进。在本文中，我们试图基于堆芯损耗谱直接和定量地揭示材料性质的隐藏信息。我们用一个简单的前馈神经网络预测了6种性质，包括3种几何性质和3种化学键性质，并取得了相当高的精度。此外，我们将所建立的模型应用于有噪声的实验谱，可以准确地预测这六种性质。这一成功的预测意味着该方法可以为材料性质的局部测量铺平道路。
Localized structures in nano- and sub-nano-scales strongly affect material properties. Thus, some spectroscopic techniques have been used to characterize local atomic and electronic structures. If material properties can be directly 'measured' via spectral observations, the atomic-scale understanding of the material properties would be dramatically facilitated. In this paper, we have attempted to unveil the hidden information about the material properties directly and quantitatively based on core-loss spectra. We predicted six properties, including three geometrical and three chemical bonding properties, by a simple feedforward neural network, and achieved considerably sufficient accuracy. Moreover, we applied the constructed model to the noisy experimental spectrum and could predict the six properties precisely. This successful prediction implies that this method can pave the way for local measurement of the material properties.