Libxc - functionals for density-functional theory

Libxc - 密度泛函理论的泛函

• 批准号: 327644371
• 负责人: Professor Dr. Miguel Marques
• 金额: --
• 依托单位: Max-Planck-Institut für Mikrostrukturphysik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2017
• 资助国家: 德国
• 起止时间: 2016-12-31 至 2020-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/327644371?language=en
• 关键词: Libxc; functionals; density; functional; theory

Density-functional theory was, without doubt, one of the greatest achievements of theoretical physics in the second half of the XXth century. It is now the workhorse theory, not only in quantum chemistry, but also in fields of atomic, molecular, and solid-state physics. At the heart of the theory, there is the famous exchange-correlation functional that has to be approximated for any practical application. For this quantity, hundreds of approximations have been proposed during the past 50 years. This project has two emphasis: the development of Libxc, a library whose purpose is to provide access to this plethora of functionals for the large community of researchers who use density functional theory; and to use the unique capabilities of Libxc to make independent benchmarks of the quality and reliability of these functionals.

密度泛函理论无疑是20世纪下半叶理论物理学最伟大的成就之一。它现在不仅在量子化学领域，而且在原子、分子和固态物理领域都是主力理论。在该理论的核心，有一个著名的交换相关函数，它必须近似于任何实际应用。对于这个量，在过去50年中提出了数百种近似值。这个项目有两个重点：Libxc的开发，这个库的目的是为使用密度泛函理论的大型研究人员社区提供访问过多函数的途径；并使用Libxc的独特功能对这些功能的质量和可靠性进行独立的基准测试。

项目成果

Efficient first-principles prediction of solid stability: Towards chemical accuracy

• DOI: 10.1038/s41524-018-0065-z
• 发表时间: 2018-03-09
• 期刊: NPJ COMPUTATIONAL MATERIALS
• 影响因子: 9.7
• 作者: Zhang, Yubo;Kitchaev, Daniil A.;Sun, Jianwei
• 通讯作者: Sun, Jianwei

Recent developments in LIBXC - A comprehensive library of functionals for density functional theory

• DOI: 10.1016/j.softx.2017.11.002
• 发表时间: 2018-01-01
• 期刊: SOFTWAREX
• 影响因子: 3.4
• 作者: Lehtola, Susi;Steigemann, Conrad;Marques, Miguel A. L.
• 通讯作者: Marques, Miguel A. L.

Exchange-correlation functionals for band gaps of solids: benchmark, reparametrization and machine learning

• DOI: 10.1038/s41524-020-00360-0
• 发表时间: 2020-07-10
• 期刊: NPJ COMPUTATIONAL MATERIALS
• 影响因子: 9.7
• 作者: Borlido, Pedro;Schmidt, Jonathan;Botti, Silvana
• 通讯作者: Botti, Silvana