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Electron charge density is a fundamental physical quantity, determiningvarious properties of matter. In this study, we have proposed a deep-learningmodel for accurate charge density prediction. Our model naturally preservesphysical symmetries and can be effectively trained from one-shot densityfunctional theory calculation toward high accuracy. It captures detailed atomicenvironment information, ensuring accurate predictions of charge density acrossbulk, surface, molecules, and amorphous structures. This implementationexhibits excellent scalability and provides efficient analyses of materialproperties in large-scale condensed matter systems.

Deep Charge: A Deep Learning Model of Electron Density from One-Shot Density Functional Theory Calculation