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On-the-Fly Computation of Frontal Orbitals in Density Matrix Expansions

机译:密度矩阵扩展中正面轨道的在飞行计算

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We propose a method for computation of frontal (homo and lumo) orbitals in recursive polynomial expansion algorithms for the density matrix. Such algorithms give a computational cost that increases only linearly with system size for sufficiently sparse systems, but a drawback compared to the traditional diagonalization approach is that molecular orbitals are not readily available. Our method is based on the idea to use the polynomial of the density matrix expansion as an eigenvalue filter giving large separation between eigenvalues around homo and lumo [Rubensson et al. J. Chem. Phys. 2008, 128, 176101]. This filter is combined with a shift-and-square (folded spectrum) method to move the desired eigenvalue to the end of the spectrum. In this work we propose a transparent way to select recursive expansion iteration and shift for the eigenvector computation that results in a sharp eigenvalue filter. The filter is obtained as a byproduct of the density matrix expansion, and there is no significant additional cost associated either with its construction or with its application. This gives a clear-cut and efficient eigenvalue solver that can be used to compute homo and lumo orbitals with sufficient accuracy in a small fraction of the total recursive expansion time. Our algorithms make use of recent homo and lumo eigenvalue estimates that can be obtained at negligible cost [Rubensson et al. SIAM J. Sci. Comput. 2014, 36, B147]. We illustrate our method by performing self-consistent field calculations for large scale systems.
机译:我们提出了一种用于计算密度矩阵的递归多项式扩展算法中的正面(HOMO和LUMO)轨道的方法。这种算法给出了计算成本,其仅随着足够稀疏的系统的系统尺寸线性增加,但与传统的对角化方法相比的缺点是分子轨道不易获得。我们的方法基于使用密度矩阵膨胀的多项式作为特征值滤波器的思想,从而在Homo和Lumo的特征值之间进行大分离[Rumensson等。 J.Chem。物理。 2008,128,176101]。该滤波器与移位和方形(折叠频谱)方法组合,以将所需的特征值移动到光谱的末端。在这项工作中,我们提出了一种透明的方法来选择递归扩展迭代并为特征向量计算转移,从而导致尖锐的特征值滤波器。优选过滤器作为密度矩阵膨胀的副产物,并且没有显着的额外成本与其建筑或其应用相关。这给出了一种清晰且有效的特征值求解器,可用于计算HOMO和LUMO轨道,以足够的精度在总递归扩展时间的一小部分中。我们的算法利用最近的同性恋和Lumo特征值估计,可以以可忽略的成本获得[Rubensson等人。暹罗J. SCI。计算。 2014,36,B147]。我们通过对大规模系统执行自我一致的现场计算来说明我们的方法。

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