  Modern laboratory techniques like ultrafast laser excitation and shock
compression can bring matter into highly nonequilibrium states with complex
structural transformation, metallization and dissociation dynamics. To
understand and model the dramatic change of both electronic structures and ion
dynamics during such dynamic processes, the traditional method faces
difficulties. Here, we demonstrate the ability of deep neural network (DNN) to
capture the atomic local-environment dependence of electronic density of states
(DOS) for both multicomponent system under exoplanet thermodynamic condition
and nonequilibrium system during super-heated melting process. Large scale and
time-resolved diagnosis of DOS can be efficiently achieved within the accuracy
of ab initio method. Moreover, the atomic contribution to DOS given by DNN
model accurately reveals the information of local neighborhood for selected
atom, thus can serve as robust order parameters to identify different phases
and intermediate local structures, strongly highlights the efficacy of this DNN
model in studying dynamic processes.
