Generating accurate density matrices on the tangent space of a Grassmann manifold

被引:3
|
作者
Tan, Jake A. [1 ]
Lao, Ka Un [1 ]
机构
[1] Virginia Commonwealth Univ, Dept Chem, Richmond, VA 23284 USA
来源
JOURNAL OF CHEMICAL PHYSICS | 2023年 / 158卷 / 05期
关键词
WAVE-FUNCTION; CONVERGENCE; ACCELERATION; FERROCENE; GEOMETRY; SYSTEMS;
D O I
10.1063/5.0137775
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Interpolating a density matrix from a set of known density matrices is not a trivial task. This is because a linear combination of density matrices does not necessarily correspond to another density matrix. In this Communication, density matrices are examined as objects of a Grassmann manifold. Although this manifold is not a vector space, its tangent space is a vector space. As a result, one can map the density matrices on this manifold to their corresponding vectors in the tangent space and then perform interpolations on that tangent space. The resulting interpolated vector can be mapped back to the Grassmann manifold, which can then be utilized (1) as an optimal initial guess for a self-consistent field (SCF) calculation or (2) to derive energy directly without time-consuming SCF iterations. Such a promising approach is denoted as Grassmann interpolation (G-Int). The hydrogen molecule has been used to illustrate that the described interpolated method in this work preserves the essential attributes of a density matrix. For phosphorus mononitride and ferrocene, it was demonstrated numerically that reference points for the definition of the corresponding tangent spaces can be chosen arbitrarily. In addition, the interpolated density matrices provide a superior and essentially converged initial guess for an SCF calculation to make the SCF procedure itself unnecessary. Finally, this accurate, efficient, robust, and systematically improved G-Int strategy has been used for the first time to generate highly accurate potential energy surfaces with fine details for the difficult case, ferrocene.
引用
收藏
页数:7
相关论文
共 50 条
  • [41] Determination of the critical manifold tangent space and curvature with Monte Carlo renormalization group
    Wu, Yantao
    Car, Roberto
    PHYSICAL REVIEW E, 2019, 100 (02)
  • [42] 2D-DLPP Algorithm Based on SPD Manifold Tangent Space
    Li, Xiaohang
    Li, Bo
    Wang, Zonghui
    ADVANCED INTELLIGENT COMPUTING TECHNOLOGY AND APPLICATIONS, ICIC 2023, PT IV, 2023, 14089 : 201 - 212
  • [43] Consistent tangent matrices for density-dependent finite plasticity models
    Pérez-Foguet, A
    Rodriguez-Ferran, A
    Huerta, A
    INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS IN GEOMECHANICS, 2001, 25 (11) : 1045 - 1075
  • [44] Manifold learning localization based on local tangent space alignment for wireless sensor networks
    Zhang, Hanyu
    Xu, Hao
    International Journal of Computers and Applications, 2024, 46 (09) : 785 - 794
  • [45] SPD Data Dimensionality Reduction based on SPD Manifold Tangent Space and Local LDA
    Yuan, Xuejing
    Huang, Xiao
    Ma, Zhengming
    5TH INTERNATIONAL CONFERENCE ON ALGORITHMS, COMPUTING AND SYSTEMS, ICACS 2021, 2021, : 68 - 73
  • [46] Combining intrinsic dimension and local tangent space for manifold spectral clustering image segmentation
    Yao, Xiaoling
    Zhang, Rongguo
    Hu, Jing
    Chang, Kai
    Liu, Xiaojun
    Zhao, Jian
    SOFT COMPUTING, 2022, 26 (18) : 9557 - 9572
  • [47] DISTRIBUTED PARTICLE FILTERS FOR STATE TRACKING ON THE STIEFEL MANIFOLD USING TANGENT SPACE STATISTICS
    Bordin, Claudio J., Jr.
    de Figueredo, Caio G.
    Bruno, Marcelo G. S.
    2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2022, : 5488 - 5492
  • [48] Combining intrinsic dimension and local tangent space for manifold spectral clustering image segmentation
    Xiaoling Yao
    Rongguo Zhang
    Jing Hu
    Kai Chang
    Xiaojun Liu
    Jian Zhao
    Soft Computing, 2022, 26 : 9557 - 9572
  • [49] Improved density matrices for accurate molecular ionization potentials
    Bruneval, Fabien
    PHYSICAL REVIEW B, 2019, 99 (04)
  • [50] VISUAL TRACKING AND DYNAMIC LEARNING ON THE GRASSMANN MANIFOLD WITH INFERENCE FROM A BAYESIAN FRAMEWORK AND STATE SPACE MODELS
    Khan, Zulfiqar Hasan
    Gu, Irene Yu-Hua
    2011 18TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2011, : 1433 - 1436