  Electron charge density is a fundamental physical quantity, determining
various properties of matter. In this study, we have proposed a deep-learning
model for accurate charge density prediction. Our model naturally preserves
physical symmetries and can be effectively trained from one-shot density
functional theory calculation toward high accuracy. It captures detailed atomic
environment information, ensuring accurate predictions of charge density across
bulk, surface, molecules, and amorphous structures. This implementation
exhibits excellent scalability and provides efficient analyses of material
properties in large-scale condensed matter systems.
