Literature DB >> 21261328

Reconsidering an analytical gradient expression within a divide-and-conquer self-consistent field approach: exact formula and its approximate treatment.

Masato Kobayashi1, Tomotaka Kunisada, Tomoko Akama, Daisuke Sakura, Hiromi Nakai.   

Abstract

An analytical energy gradient formula for the density-matrix-based linear-scaling divide-and-conquer (DC) self-consistent field (SCF) method was proposed in a previous paper by Yang and Lee (YL) [J. Chem. Phys. 103, 5674 (1995)]. Since the formula by YL does not correspond to the exact gradient of the DC-SCF energy, we derive the exact formula by direct differentiation, which requires solving the coupled-perturbed equations while including the inter-subsystem coupling terms. Next, we present an alternative formula for approximately evaluating the DC-SCF energy gradient, assuming the variational condition for the subsystem density matrices. Numerical assessments confirmed that the DC-SCF energy gradient values obtained by the present formula are in reasonable agreement with the conventional SCF values when adopting a reliable buffer region. Furthermore, the performance of the present method was found to be better than that of the YL method.

Year:  2011        PMID: 21261328     DOI: 10.1063/1.3524337

Source DB:  PubMed          Journal:  J Chem Phys        ISSN: 0021-9606            Impact factor:   3.488


  4 in total

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Authors:  Timothy J Giese; Darrin M York
Journal:  J Phys Condens Matter       Date:  2017-08-17       Impact factor: 2.333

2.  Liquid water simulations with the density fragment interaction approach.

Authors:  Xiangqian Hu; Yingdi Jin; Xiancheng Zeng; Hao Hu; Weitao Yang
Journal:  Phys Chem Chem Phys       Date:  2012-04-02       Impact factor: 3.676

3.  A variational linear-scaling framework to build practical, efficient next-generation orbital-based quantum force fields.

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Journal:  J Chem Theory Comput       Date:  2013-03-12       Impact factor: 6.006

Review 4.  Computational and data driven molecular material design assisted by low scaling quantum mechanics calculations and machine learning.

Authors:  Wei Li; Haibo Ma; Shuhua Li; Jing Ma
Journal:  Chem Sci       Date:  2021-11-08       Impact factor: 9.825

  4 in total

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