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

Abstract

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.

7 Figures, accepted by PRB

References in corpus (3)

• Advanced capabilities for materials modelling with Quantum ESPRESSO
• Accelerating Finite-temperature Kohn-Sham Density Functional Theory with Deep Neural Networks
• Ultrafast photoluminescence in metals: Theory and its application to silver

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