Focusing on region 3, which is the lower edge of the conduction band, it is confirmed that high prediction accuracy can be achieved when using the Si-K edge with -1 eV to 9 eV. This seems natural when considering that Si-K edge reflects the p-orbitals of the conduction band in excited states, and there exists at least some correlation with the p-orbitals of the conduction band in the ground state. In contrast, for region 2, similar to the case with the s-PDOS, it is found that the highest prediction accuracy is also achieved when using the Si-K edge with 9 eV to 14 eV. This implies that the information related to the hybridization of s-and p-orbitals is primarily contained within the 9 eV to 14 eV range. These results suggest that that the ML model successfully extracts information related to the hybridization and allows the prediction of not only the p-orbitals in the conduction band but also s-orbitals in the valence band. found from the Fig. . Firstly, the absolute value of ΔMSE tends to be smaller when it is negative, whereas the larger absolute value is often obtained when ΔMSE is positive, indicating that while template matching can be alternative to ML models in some cases, ML generally leads to higher prediction accuracy than the template matching. The second point is that when the MSE of the Si-K edge is large, indicating that it is difficult to find spectra with similar shapes within the database, the ΔMSE tends to be positive. This clearly indicates that the ML becomes more effective when there are no similar spectra in the database. Thus, compared to template matching, the utilization of ML models has generally been shown to be effective.