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自动生成显式相关方法的补充辅助基集。

Automatic generation of complementary auxiliary basis sets for explicitly correlated methods.

机构信息

Department of Molecular Chemistry and Materials Science, Weizmann Institute of Science, Reḥovot, Israel.

出版信息

J Comput Chem. 2022 Sep 30;43(25):1690-1700. doi: 10.1002/jcc.26970. Epub 2022 Jul 19.

Abstract

Explicitly correlated calculations, aside from the orbital basis set, typically require three auxiliary basis sets: Coulomb-exchange fitting (JK), resolution of the identity MP2 (RI-MP2), and complementary auxiliary basis set (CABS). If unavailable for the orbital basis set and chemical elements of interest, the first two can be auto-generated on the fly using existing algorithms, but not the third. In this paper, we present a quite simple algorithm named autoCABS; a Python implementation under a free software license is offered at Github. For the cc-pVnZ-F12 (n = D,T,Q,5), the W4-08 thermochemical benchmark, and the HFREQ2014 set of harmonic frequencies, we demonstrate that autoCABS-generated CABS basis sets are comparable in quality to purpose-optimized OptRI basis sets from the literature, and that the quality difference becomes entirely negligible as n increases.

摘要

显关联计算,除了轨道基组外,通常还需要三个辅助基组:库仑交换拟合 (JK)、积分赝势 MP2 (RI-MP2) 和补充辅助基组 (CABS)。如果轨道基组和感兴趣的化学元素不可用,则可以使用现有的算法即时自动生成前两个,但不能生成第三个。在本文中,我们提出了一种名为 autoCABS 的相当简单的算法;在 Github 上提供了一个基于自由软件许可证的 Python 实现。对于 cc-pVnZ-F12(n = D,T,Q,5)、W4-08 热化学基准和 HFREQ2014 组谐波频率,我们证明了 autoCABS 生成的 CABS 基组在质量上与文献中来自目的优化的 OptRI 基组相当,并且随着 n 的增加,质量差异变得完全可以忽略不计。

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