Qin Langyuan, Wang Zikuan, Suo Bingbing
Institute of Modern Physics, Shaanxi Key Laboratory of Theoretical Physic Frontiers, Northwest University, Xi'an 710069, P. R. China.
Max-Planck-Institut für Kohlenforschung, Kaiser-Wilhelm-Platz 1, Mülheim an der Ruhr 45470, Germany.
J Chem Theory Comput. 2024 Oct 22;20(20):8921-8933. doi: 10.1021/acs.jctc.4c00893. Epub 2024 Oct 4.
A novel doubly iterative self-consistent field (SCF) approach using a semiempirical model Hamiltonian (denoted as the SMH algorithm) is proposed to accelerate the Hartree-Fock (HF) and density functional theory (DFT) calculations. This method first constructs the Fock matrix exactly in each SCF macroiteration, followed by a few SCF microiterations, where the Fock matrix is incrementally updated using an inexpensive semiempirical approximation. This leads to an improved wave function in each SCF macroiteration with minimal additional cost, and therefore a reduced number of exact Fock builds is required for SCF convergence. The SMH algorithm can be combined with conventional SCF convergence techniques such as level shifting, direct inversion in the iterative subspace (DIIS), and energy-DIIS (EDIIS). When integrated with DIIS, SMH enhances the convergence of simple organic molecules by approximately 10% compared to plain DIIS, with speedups of up to 60% for the more challenging transition metal systems compared to EDIIS + DIIS. Our results show that SMH is a reliable SCF accelerator that seldom deteriorates convergence and is highly robust.
提出了一种使用半经验模型哈密顿量的新型双重迭代自洽场(SCF)方法(称为SMH算法),以加速哈特里 - 福克(HF)和密度泛函理论(DFT)计算。该方法首先在每个SCF宏观迭代中精确构建福克矩阵,然后进行几次SCF微观迭代,其中福克矩阵使用廉价的半经验近似进行增量更新。这导致在每个SCF宏观迭代中以最小的额外成本改进波函数,因此SCF收敛所需的精确福克构建次数减少。SMH算法可以与传统的SCF收敛技术相结合,如能级移动、迭代子空间中的直接反演(DIIS)和能量DIIS(EDIIS)。与普通DIIS相比,当与DIIS集成时,SMH可将简单有机分子的收敛性提高约10%,与EDIIS + DIIS相比,对于更具挑战性的过渡金属系统,加速比高达60%。我们的结果表明,SMH是一种可靠的SCF加速器,很少会降低收敛性,并且具有高度的鲁棒性。