Deep Charge: Deep learning model of electron density from a one-shot density functional theory calculation
Taoyuze Lv, Zhicheng Zhong, Yuhang Liang, Feng Li, Jun Huang, Rongkun Zheng
Abstract
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.
Topics & Concepts
Density functional theoryCharge (physics)Charge densityWarm dense matterElectronPhysicsScalabilityStatistical physicsElectron densityComputational physicsComputer scienceQuantum mechanicsDatabaseMachine Learning in Materials ScienceElectronic and Structural Properties of OxidesAdvanced Chemical Physics Studies