.png){kind=icon}
返回
Self-consistent electron density with shell structure using neural network-based Pauli potential
.png){kind=icon}
PRE
AI
DOI：10.1063/5.0239416.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The orbital-free density functional theory (OF-DFT) based method is a convenient tool to carry out electronic structure calculations scaling almost linearly with the number of electrons. However, the main impediment in the application of this method is the unavailability of the accurate form for the non-interacting kinetic energy functional in terms of electron density. The Pauli kinetic energy functional is the unknown part of the kinetic energy functional, and the corresponding Pauli potential appears in the governing Euler equation. In the present study, we present a feed-forward neural network (NN) approach to represent the Pauli potential of a group of atomic systems possessing spherically symmetric ground-state densities. This NN-based representation of Pauli potential combined with the Hohenberg-Kohn variational principle yields self-consistent radial densities that accurately exhibit the correct atomic shell structure. For this approach, the electron density in the form of a grid serves as the input to the NN model. In addition, we calculated the non-interacting kinetic energy by summing the Pauli kinetic energy, derived from the NN-based Pauli potential, and the von Weizs & auml;cker kinetic energy. Our results demonstrate high accuracy for smaller atoms, while larger atoms exhibit greater deviations when compared with smaller atoms. The method presented in this paper provides an efficient way to calculate the Pauli potential and the Pauli kinetic energy without the need for functional derivatives. Our study represents a significant step forward in the application of machine learning techniques to OF-DFT, showcasing the potential of NNs in improving the accuracy and efficiency of quantum mechanical calculations in atomic systems.
Keyword:
KINETIC-ENERGY DENSITY
THOMAS-FERMI
GROUND-STATE
SQUARE-ROOT
ATOMS
VIRIAL
EQUATION
TERMS
期刊
IF:
3.1
论文数:
7.2W
被引数:
23.2W
机构
引用论文
分享收藏Analysis of the terminus region of the Bacillus subtilis chromosome枯草芽孢杆菌(Bacillus subtilis)染色体末端区域的分析.png)
Nature
NatureIF0
PREAI
分享收藏Interactive Effects of Polylactic Acid with Different Aluminum Concentrations on Growth, Pigment Concentrations, and Carbohydrate Accumulation of Azolla聚乳酸与不同铝浓度对Azolla的生长，颜料浓度和碳水化合物积累的相互作用
OAAI
OAAI分享收藏Machine Learning Approaches toward Orbital-free Density Functional Theory: Simultaneous Training on the Kinetic Energy Density Functional and Its Functional Derivative无轨道密度泛函理论的机器学习方法: 动能密度泛函及其泛函导数的同时训练OAAI
OAAI分享收藏分享收藏Vis/NIR hyperspectral imaging to assess freshness of sardines (Sardina pilchardus)可见光/近红外高光谱成像评估沙丁鱼（Sardina pilchardus）的新鲜度
PREAI
分享收藏ELF magnetic field measurement and calculation in 500 kV gas insulated substation500 kV气体绝缘变电站的ELF磁场测量与计算
PREAI
分享收藏
查看更多内容