使用自适应压缩交换的全电位LAPW中的距离分离混合函数

IF 11.9 1区 材料科学 Q1 CHEMISTRY, PHYSICAL
Jānis Užulis, Aleksandr V. Sorokin, Andris Gulans
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引用次数: 0

摘要

自适应压缩交换(ACE)操作符是Fock交换的低阶表示,避免了任何精度损失。本文给出了该方法在线性化增广平面波(LAPW)的形式中对具有距离分离的混合泛函的应用。为此,我们采用伪电荷方法扩展了lapw专用泊松解算器的功能,用于短期和远程相互作用核。为了使这些计算更实惠,我们修改了伪电荷方法中最昂贵的步骤,并降低了它们的计算复杂度。因此,这个实现是朝着使用LAPW计算原子数量的立方尺度混合计算迈出的第一步。我们应用我们的代码来评估文献中使用混合泛函计算的带隙的数值质量,采用由26种材料组成的测试集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Range-separated hybrid functionals in full-potential LAPW using adaptively compressed exchange

Range-separated hybrid functionals in full-potential LAPW using adaptively compressed exchange

The adaptively compressed exchange (ACE) operator is a low-rank representation of the Fock exchange, avoiding any loss of precision. We present an application of this method in the formalism of linearized augmented plane waves (LAPW) to hybrid functionals with range separation. For this purpose, we extend the functionality of the LAPW-specific Poisson solver employing the pseudocharge method for the short- and long-range interaction kernels. To make these calculations more affordable, we revise the most expensive steps in the pseudocharge method and reduce their computational complexity. As a result, this implementation is a first step towards cubic-scaling hybrid calculations employing LAPW with respect to the number of atoms. We apply our code for assessing the numerical quality of band gaps computed with hybrid functionals in the literature, employing a test set consisting of 26 materials.

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来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
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