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Modern laboratory techniques like ultrafast laser excitation and shockcompression can bring matter into highly nonequilibrium states with complexstructural transformation, metallization and dissociation dynamics. Tounderstand and model the dramatic change of both electronic structures and iondynamics during such dynamic processes, the traditional method facesdifficulties. Here, we demonstrate the ability of deep neural network (DNN) tocapture the atomic local-environment dependence of electronic density of states(DOS) for both multicomponent system under exoplanet thermodynamic conditionand nonequilibrium system during super-heated melting process. Large scale andtime-resolved diagnosis of DOS can be efficiently achieved within the accuracyof ab initio method. Moreover, the atomic contribution to DOS given by DNNmodel accurately reveals the information of local neighborhood for selectedatom, thus can serve as robust order parameters to identify different phasesand intermediate local structures, strongly highlights the efficacy of this DNNmodel in studying dynamic processes.

Towards Large-Scale and Spatio-temporally Resolved Diagnosis of Electronic Density of States by Deep Learning