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.
@article{arxiv.2309.14638,
title = {Deep Charge: A Deep Learning Model of Electron Density from One-Shot Density Functional Theory Calculation},
author = {Taoyuze Lv and Zhicheng Zhong and Yuhang Liang and Feng Li and Jun Huang and Rongkun Zheng},
journal= {arXiv preprint arXiv:2309.14638},
year = {2023}
}