Quantitative estimation of properties from core-loss spectrum via neural network

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.