密度泛函理论
Kohn-Sham方程
张量(固有定义)
缩放比例
加速
柯西应力张量
障碍物
计算机科学
线性比例尺
统计物理学
电荷(物理)
材料科学
人工智能
物理
量子力学
数学
并行计算
几何学
大地测量学
政治学
纯数学
法学
地理
作者
Beatriz González del Río,Brandon Phan,Rampi Ramprasad
标识
DOI:10.1038/s41524-023-01115-3
摘要
Abstract Density functional theory (DFT) has been a critical component of computational materials research and discovery for decades. However, the computational cost of solving the central Kohn–Sham equation remains a major obstacle for dynamical studies of complex phenomena at-scale. Here, we propose an end-to-end machine learning (ML) model that emulates the essence of DFT by mapping the atomic structure of the system to its electronic charge density, followed by the prediction of other properties such as density of states, potential energy, atomic forces, and stress tensor, by using the atomic structure and charge density as input. Our deep learning model successfully bypasses the explicit solution of the Kohn-Sham equation with orders of magnitude speedup (linear scaling with system size with a small prefactor), while maintaining chemical accuracy. We demonstrate the capability of this ML-DFT concept for an extensive database of organic molecules, polymer chains, and polymer crystals.
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