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首页> 外文期刊>Journal of Computational Physics >Adaptive local basis set for Kohn-Sham density functional theory in a discontinuous Galerkin framework I: Total energy calculation
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Adaptive local basis set for Kohn-Sham density functional theory in a discontinuous Galerkin framework I: Total energy calculation

机译:不连续Galerkin框架中Kohn-Sham密度泛函理论的自适应局部基础集I:总能量计算

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摘要

Kohn-Sham density functional theory is one of the most widely used electronic structure theories. In the pseudopotential framework, uniform discretization of the Kohn-Sham Hamiltonian generally results in a large number of basis functions per atom in order to resolve the rapid oscillations of the Kohn-Sham orbitals around the nuclei. Previous attempts to reduce the number of basis functions per atom include the usage of atomic orbitals and similar objects, but the atomic orbitals generally require fine tuning in order to reach high accuracy. We present a novel discretization scheme that adaptively and systematically builds the rapid oscillations of the Kohn-Sham orbitals around the nuclei as well as environmental effects into the basis functions. The resulting basis functions are localized in the real space, and are discontinuous in the global domain. The continuous Kohn-Sham orbitals and the electron density are evaluated from the discontinuous basis functions using the discontinuous Galerkin (DG) framework. Our method is implemented in parallel and the current implementation is able to handle systems with at least thousands of atoms. Numerical examples indicate that our method can reach very high accuracy (less than 1. meV) with a very small number (4-40) of basis functions per atom.
机译:Kohn-Sham密度泛函理论是应用最广泛的电子结构理论之一。在伪势框架中,Kohn-Sham哈密顿量的均匀离散化通常会导致每个原子具有大量基函数,以便解决核周围Kohn-Sham轨道的快速振荡。先前减少每个原子的基函数数量的尝试包括使用原子轨道和类似物体,但是原子轨道通常需要进行微调以达到高精度。我们提出了一种新颖的离散化方案,该方案可以自适应地系统地构建围绕核的Kohn-Sham轨道的快速振荡以及环境效应成为基本函数。生成的基函数位于实际空间中,并且在全局域中不连续。连续的Kohn-Sham轨道和电子密度是使用不连续的Galerkin(DG)框架从不连续的基函数进行评估的。我们的方法是并行实现的,当前的实现能够处理至少具有数千个原子的系统。数值示例表明,我们的方法可以达到非常高的精度(小于1. meV),每个原子只有很少数量(4-40)的基函数。

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