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arXiv 1604.06830math.NAcs.NAphysics.comp-ph
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通过随机化和细化计算Kohn-Sham子空间的局部化表示

Computing localized representations of the kohn-sham subspace via randomization and refinement

University of California, Berkeley(加州大学伯克利分校);Lawrence Berkeley National Laboratory(劳伦斯伯克利国家实验室);Stanford University(斯坦福大学)

Anil Damle, Lin Lin, Lexing Ying

AI总结:

本文提出一种两阶段近似列选择策略,用于高效构建Kohn-Sham子空间的局部化基组,该方法在计算成本显著降低的情况下,展示了在BH3NH3分子解离、烷链和超细胞中的有效性,比原始SCDM方法快30倍。

摘要:

Kohn-Sham子空间的局部化表示在量子化学和材料科学中具有重要作用。最近开发的密度矩阵选定列(SCDM)方法[J. Chem. Theory Comput. 11, 1463, 2015]是一种简单且稳健的程序,用于从绝缘系统中找到Kohn-Sham轨道的局部化表示。SCDM方法允许直接构建一个良好条件(甚至正交)且局部化的基组用于Kohn-Sham子空间。SCDM算法避免了优化过程,并不依赖任何可调参数。SCDM方法中最计算昂贵的步骤是列 pivoted QR 分解,用于识别构建局部化基组的重要列。在本文中,我们开发了一种两阶段近似列选择策略,以在计算成本显著降低的情况下找到重要列。我们通过BH3NH3分子解离过程、烷链和包含256个水分子的超细胞来展示该过程的有效性。大规模水分子集合的数值结果表明,两阶段局部化过程比原始SCDM算法快超过30倍,并与流行的Wannier90包相比表现良好。

英文摘要:

Localized representation of the Kohn-Sham subspace plays an important role in quantum chemistry and materials science. The recently developed selected columns of the density matrix (SCDM) method [J. Chem. Theory Comput. 11, 1463, 2015] is a simple and robust procedure for finding a localized representation of a set of Kohn-Sham orbitals from an insulating system. The SCDM method allows the direct construction of a well conditioned (or even orthonormal) and localized basis for the Kohn-Sham subspace. The SCDM algorithm avoids the use of an optimization procedure and does not depend on any adjustable parameters. The most computationally expensive step of the SCDM method is a column pivoted QR factorization that identifies the important columns for constructing the localized basis set. In this paper, we develop a two stage approximate column selection strategy to find the important columns at much lower computational cost. We demonstrate the effectiveness of this process using the dissociation process of a BH$_3$NH$_3$ molecule, an alkane chain and a supercell with 256 water molecules. Numerical results for the large collection of water molecules show that two stage localization procedure can be more than 30 times faster than the original SCDM algorithm and compares favorably with the popular Wannier90 package.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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