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一种新的基于 Lanczos 的算法,用于模拟高频二维电子自旋共振谱。

A new Lanczos-based algorithm for simulating high-frequency two-dimensional electron spin resonance spectra.

机构信息

Department of Chemistry, National Tsing Hua University, Hsinchu 30013, Taiwan.

出版信息

J Chem Phys. 2011 Jan 21;134(3):034112. doi: 10.1063/1.3523576.

Abstract

The Lanczos algorithm (LA) is a useful iterative method for the reduction of a large matrix to tridiagonal form. It is a storage efficient procedure requiring only the preceding two Lanczos vectors to compute the next. The quasi-minimal residual (QMR) method is a powerful method for the solution of linear equation systems, Ax = b. In this report we provide another application of the QMR method: we incorporate QMR into the LA to monitor the convergence of the Lanczos projections in the reduction of large sparse matrices. We demonstrate that the combined approach of the LA and QMR can be utilized efficiently for the orthogonal transformation of large, but sparse, complex, symmetric matrices, such as are encountered in the simulation of slow-motional 1D- and 2D-electron spin resonance (ESR) spectra. Especially in the 2D-ESR simulations, it is essential that we store all of the Lanczos vectors obtained in the course of the LA recursions and maintain their orthogonality. In the LA-QMR application, the QMR weight matrix mitigates the problem that the Lanczos vectors lose orthogonality after many LA projections. This enables substantially more Lanczos projections, as required to achieve convergence for the more challenging ESR simulations. It, therefore, provides better accuracy for the eigenvectors and the eigenvalues of the large sparse matrices originating in 2D-ESR simulations than does the previously employed method, which is a combined approach of the LA and the conjugate-gradient (CG) methods, as evidenced by the quality and convergence of the 2D-ESR simulations. Our results show that very slow-motional 2D-ESR spectra at W-band (95 GHz) can be reliably simulated using the LA-QMR method, whereas the LA-CG consistently fails. The improvements due to the LA-QMR are of critical importance in enabling the simulation of high-frequency 2D-ESR spectra, which are characterized by their very high resolution to molecular orientation.

摘要

Lanczos 算法(LA)是一种将大型矩阵约化为三对角形式的有用迭代方法。它是一种存储效率高的过程,仅需前两个 Lanczos 向量即可计算下一个。拟最小残差(QMR)方法是求解线性方程组 Ax=b 的强大方法。在本报告中,我们提供了 QMR 方法的另一种应用:我们将 QMR 纳入 LA 中,以监测大型稀疏矩阵约化过程中 Lanczos 投影的收敛性。我们证明,LA 和 QMR 的组合方法可有效地用于正交变换大型稀疏复杂对称矩阵,例如在慢动 1D 和 2D 电子自旋共振(ESR)谱模拟中遇到的矩阵。特别是在 2D-ESR 模拟中,我们必须存储在 LA 递归过程中获得的所有 Lanczos 向量,并保持它们的正交性。在 LA-QMR 应用中,QMR 权矩阵减轻了 Lanczos 向量在多次 LA 投影后失去正交性的问题。这使得需要进行更多的 Lanczos 投影才能实现更具挑战性的 ESR 模拟的收敛。因此,与以前采用的 LA 和共轭梯度(CG)方法的组合方法相比,它为来自 2D-ESR 模拟的大型稀疏矩阵的特征向量和特征值提供了更好的准确性,这可以从 2D-ESR 模拟的质量和收敛性得到证明。我们的结果表明,非常缓慢的 W 波段(95 GHz)2D-ESR 谱可以使用 LA-QMR 方法可靠地模拟,而 LA-CG 则始终失败。LA-QMR 的改进对于能够模拟高频 2D-ESR 谱至关重要,高频 2D-ESR 谱的特点是其对分子取向的分辨率非常高。

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