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基于误差反向传播算法的直接吉文斯旋转法用于自洽场求解。

Direct Givens rotation method based on error back-propagation algorithm for self-consistent field solution.

作者信息

Oshima Rei, Nakai Hiromi

机构信息

Department of Chemistry and Biochemistry, School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku, Tokyo 169-8555, Japan.

Waseda Research Institute for Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku, Tokyo 169-8555, Japan.

出版信息

J Chem Phys. 2025 Jan 7;162(1). doi: 10.1063/5.0232518.

Abstract

The self-consistent field (SCF) procedure is the standard technique for solving the Hartree-Fock and Kohn-Sham density functional theory calculations, while convergence is not theoretically guaranteed. Direct minimization methods, such as the augmented Lagrangian method (ALM) and second-order SCF (SOSCF), obtain the SCF solution by minimizing the Lagrangian with the gradient. In SOSCF, molecular orbitals are optimized by truncating the Taylor expansion of a unitary matrix represented in exponential form to ensure the orthonormality condition. This study proposes an alternative algorithm for direct-energy minimization to obtain an SCF solution using ALM Lagrangian by adopting sequential Givens rotations between occupied and virtual orbitals. The Givens rotation corresponds to unitary transformations that guarantee orthogonality and avoid variational collapse. Complex gradients for sequential Givens rotation were obtained by the error back-propagation method, which is based on the chain rule. Illustrative applications clarified the features of the present DGR methods by comparing with other SCF algorithms such as direct inversion in iterative subspace, SOSCF, and ALM.

摘要

自洽场(SCF)方法是求解哈特里 - 福克和科恩 - 沙姆密度泛函理论计算的标准技术,但其收敛性在理论上并无保证。直接极小化方法,如增广拉格朗日方法(ALM)和二阶SCF(SOSCF),通过用梯度极小化拉格朗日函数来获得SCF解。在SOSCF中,分子轨道通过截断以指数形式表示的酉矩阵的泰勒展开来优化,以确保正交归一条件。本研究提出了一种用于直接能量极小化的替代算法,通过在占据轨道和虚拟轨道之间采用顺序吉文斯旋转,利用ALM拉格朗日函数来获得SCF解。吉文斯旋转对应于保证正交性并避免变分坍缩的酉变换。顺序吉文斯旋转的复梯度通过基于链式法则的误差反向传播方法获得。通过与其他SCF算法(如迭代子空间中的直接反演、SOSCF和ALM)进行比较,示例应用阐明了当前DGR方法的特点。

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