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arXiv:2409.10851 (physics)
[Submitted on 17 Sep 2024 (v1) , last revised 1 Oct 2024 (this version, v2)]

Title: A gradient flow model for ground state calculations in Wigner formalism based on density functional theory

Title: 基于密度泛函理论的Wigner形式中基态计算的梯度流模型

Authors:Guanghui Hu, Ruo Li, Hongfei Zhan
Abstract: In this paper, a gradient flow model is proposed for conducting ground state calculations in Wigner formalism of many-body system in the framework of density functional theory. More specifically, an energy functional for the ground state in Wigner formalism is proposed to provide a new perspective for ground state calculations of the Wigner function. Employing density functional theory, a gradient flow model is designed based on the energy functional to obtain the ground state Wigner function representing the whole many-body system. Subsequently, an efficient algorithm is developed using the operator splitting method and the Fourier spectral collocation method, whose numerical complexity of single iteration is $O(n_{\rm DoF}\log n_{\rm DoF})$. Numerical experiments demonstrate the anticipated accuracy, encompassing the one-dimensional system with up to $2^{21}$ particles and the three-dimensional system with defect, showcasing the potential of our approach to large-scale simulations and computations of systems with defect.
Abstract: 在本文中,提出了一种梯度流模型,用于在密度泛函理论框架下进行多体系统的Wigner形式的基态计算。 更具体地说,提出了一个用于Wigner形式基态的能量泛函,以提供一种新的视角来计算Wigner函数的基态。 利用密度泛函理论,基于能量泛函设计了一个梯度流模型,以获得表示整个多体系统的基态Wigner函数。 随后,开发了一种高效的算法,该算法使用算子分裂方法和傅里叶谱插值方法,其单次迭代的数值复杂度为$O(n_{\rm DoF}\log n_{\rm DoF})$。 数值实验展示了预期的精度,包括最多含有$2^{21}$个粒子的一维系统和具有缺陷的三维系统,展示了我们方法在大规模模拟和计算有缺陷系统的潜力。
Comments: 20 pages, 6 figures
Subjects: Computational Physics (physics.comp-ph) ; Mathematical Physics (math-ph)
MSC classes: 65M70, 70G60, 81S30
Cite as: arXiv:2409.10851 [physics.comp-ph]
  (or arXiv:2409.10851v2 [physics.comp-ph] for this version)
  https://doi.org/10.48550/arXiv.2409.10851
arXiv-issued DOI via DataCite

Submission history

From: Hongfei Zhan [view email]
[v1] Tue, 17 Sep 2024 02:38:01 UTC (810 KB)
[v2] Tue, 1 Oct 2024 06:43:14 UTC (810 KB)
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