Learning excited states from ground states by using an artificial neural network

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 machine leaning.