Excited states are different quantum states from their ground states, and spectroscopy
methods that can assess excited states are widely used in materials characterization.
Understanding the spectra reflecting excited states is thus of great importance for
materials science. However, understanding such spectra remains difficult because excited
states have usually different atomic or electronic configurations from their corresponding
ground states. If excited states could be predicted from ground states, the knowledge
of the excited states would be improved. Here, we used an artificial neural network
to predict the excited states of the core-electron absorption spectra from their ground
states. Consequently, our model correctly learned and predicted the excited states
from their ground states, providing several thousand times computational efficiency.
Furthermore, it showed excellent transferability to other materials. Also, we found
two physical insights about excited states: core-hole effects of amorphous silicon
oxides are stronger than those of crystalline silicon oxides, and the excited-ground
states relationships of some metal oxides are similar to those of the silicon oxides,
which could not be obtained by conventional spectral simulation nor found until using