Mizoguchi Teruyasu, Kiyohara Shin
Institute of Industrial Science, The University of Tokyo, Komaba, Tokyo 113-8505, Japan.
Microscopy (Oxf). 2020 Apr 8;69(2):92-109. doi: 10.1093/jmicro/dfz109.
Materials characterization is indispensable for materials development. In particular, spectroscopy provides atomic configuration, chemical bonding and vibrational information, which are crucial for understanding the mechanism underlying the functions of a material. Despite its importance, the interpretation of spectra using human-driven methods, such as manual comparison of experimental spectra with reference/simulated spectra, is becoming difficult owing to the rapid increase in experimental spectral data. To overcome the limitations of such methods, we develop new data-driven approaches based on machine learning. Specifically, we use hierarchical clustering, a decision tree and a feedforward neural network to investigate the electron energy loss near edge structures (ELNES) spectrum, which is identical to the X-ray absorption near edge structure (XANES) spectrum. Hierarchical clustering and the decision tree are used to interpret and predict ELNES/XANES, while the feedforward neural network is used to obtain hidden information about the material structure and properties from the spectra. Further, we construct a prediction model that is robust against noise by data augmentation. Finally, we apply our method to noisy spectra and predict six properties accurately. In summary, the proposed approaches can pave the way for fast and accurate spectrum interpretation/prediction as well as local measurement of material functions.
材料表征对于材料开发至关重要。特别是，光谱学提供了原子构型、化学键合和振动信息，这些对于理解材料功能背后的机制至关重要。尽管其很重要，但由于实验光谱数据的迅速增加，使用人工驱动的方法（如将实验光谱与参考/模拟光谱进行手动比较）来解释光谱变得困难。为了克服此类方法的局限性，我们基于机器学习开发了新的数据驱动方法。具体而言，我们使用层次聚类、决策树和前馈神经网络来研究电子能量损失近边结构（ELNES）光谱，它与X射线吸收近边结构（XANES）光谱相同。层次聚类和决策树用于解释和预测ELNES/XANES，而前馈神经网络用于从光谱中获取有关材料结构和性质的隐藏信息。此外，我们通过数据增强构建了一个对噪声具有鲁棒性的预测模型。最后，我们将我们的方法应用于噪声光谱并准确预测了六种性质。总之，所提出的方法可为快速准确的光谱解释/预测以及材料功能的局部测量铺平道路。
