Chapter 17: Machine Learning for Core-loss Spectrum
Published:15 Jul 2020
T. Mizoguchi and S. Kiyohara, in Machine Learning in Chemistry
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Characterization is indispensable for developing functional materials and molecules. In particular, spectroscopy provides atomic configuration, chemical bonding, and vibrational information, which are crucial for understanding the mechanism underlying the functions of a material and molecule. 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 increase in experimental data. To overcome the limitations of “human-driven” methods, new data-driven approaches based on machine learning were developed. In this chapter, we review our machine learning method for spectral analysis. Hierarchical clustering, a decision tree, and a feedforward neural network were combined to investigate the core loss spectroscopy, namely 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 the local measurement of material functions.