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Random-Phase Approximation Methods
Annual Review of Physical Chemistry ( IF 14.7 ) Pub Date : 2017-05-02 00:00:00 , DOI: 10.1146/annurev-physchem-040215-112308
Guo P. Chen 1 , Vamsee K. Voora 1 , Matthew M. Agee 1 , Sree Ganesh Balasubramani 1 , Filipp Furche 1
Affiliation  

Random-phase approximation (RPA) methods are rapidly emerging as cost-effective validation tools for semilocal density functional computations. We present the theoretical background of RPA in an intuitive rather than formal fashion, focusing on the physical picture of screening and simple diagrammatic analysis. A new decomposition of the RPA correlation energy into plasmonic modes leads to an appealing visualization of electron correlation in terms of charge density fluctuations. Recent developments in the areas of beyond-RPA methods, RPA correlation potentials, and efficient algorithms for RPA energy and property calculations are reviewed. The ability of RPA to approximately capture static correlation in molecules is quantified by an analysis of RPA natural occupation numbers. We illustrate the use of RPA methods in applications to small-gap systems such as open-shell d- and f-element compounds, radicals, and weakly bound complexes, where semilocal density functional results exhibit strong functional dependence.

中文翻译:


随机相位逼近方法

随机相位逼近(RPA)方法正迅速成为具有成本效益的半局部密度函数计算验证工具。我们以直观而非正式的方式介绍RPA的理论背景,重点是筛选和简单图解分析的物理图片。RPA相关能重新分解为等离激元模式后,就可以根据电荷密度波动吸引人的电子相关性可视化。回顾了超出RPA方法,RPA相关电位以及RPA能量和特性计算的高效算法领域的最新进展。RPA近似捕获分子中静态相关性的能力是通过对RPA自然占据数的分析来量化的。df元素的化合物,自由基和弱结合的络合物,其中半局部密度泛函结果显示出强烈的泛函依赖性。

更新日期:2017-05-02
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