一种仅使用O(1)个梯度的快速、准确的半数值Hessian算法。

IF 5.5 1区 化学 Q2 CHEMISTRY, PHYSICAL
Bo Wang, Shaohang Luo, Zikuan Wang, Wenjian Liu
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引用次数: 0

摘要

在这项工作中,我们描述了一种新的算法,O1NumHess,通过在O(1)位移几何上计算梯度的有限微分来计算分子系统的Hessian,而不是像传统的半数值Hessian算法那样在O(1)位移几何(其中Natom是原子数)上计算梯度。减少梯度数的关键是Hessian的非对角线低秩(ODLR)性质,即Hessian的块对应于两个相距较远的低秩原子群。该属性将Hessian的独立条目数从O(Natom2)减少到O(Natom),这样O(1)个梯度已经包含足够的信息来唯一地确定Hessian。使用BDF程序对模型体系(长烷烃和多烯)、过渡金属反应(WCCR10组)和非共价配合物(S30L-CI组)的数值结果表明,O1NumHess给出的频率、零点能量、焓和吉布斯自由能误差仅为传统双面半数值Hessians的两倍左右。此外,O1NumHess总是比传统的数值Hessian算法快,有时甚至比解析Hessian算法快,并且对于足够大的系统只需要大约100个梯度。这个方法的一个开源实现,也可以应用于与计算化学无关的问题,可以在GitHub上找到。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
O1NumHess: A Fast and Accurate Seminumerical Hessian Algorithm Using Only O(1) Gradients.

In this work, we describe a new algorithm, O1NumHess, to calculate the Hessian of a molecular system by finite differentiation of gradients calculated at O(1) displaced geometries, instead of O(Natom) displaced geometries (where Natom is the number of atoms) as in the conventional seminumerical Hessian algorithm. Key to the reduction of the number of gradients is the off-diagonal low-rank (ODLR) property of Hessians, namely the blocks of the Hessian that correspond to two distant groups of atoms having low rank. This property reduces the number of independent entries of the Hessian from O(Natom2) to O(Natom), such that O(1) gradients already contain enough information to uniquely determine the Hessian. Numerical results on model systems (long alkanes and polyenes), transition metal reactions (the WCCR10 set), and noncovalent complexes (the S30L-CI set) using the BDF program show that O1NumHess gives frequency, zero-point energy, enthalpy, and Gibbs free energy errors that are only about two times those of conventional double-sided seminumerical Hessians. Moreover, O1NumHess is always faster than the conventional numerical Hessian algorithm, frequently even faster than the analytic Hessian, and requires only about 100 gradients for sufficiently large systems. An open-source implementation of this method, which can also be applied to problems irrelevant to computational chemistry, is available on GitHub.

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来源期刊
Journal of Chemical Theory and Computation
Journal of Chemical Theory and Computation 化学-物理:原子、分子和化学物理
CiteScore
9.90
自引率
16.40%
发文量
568
审稿时长
1 months
期刊介绍: The Journal of Chemical Theory and Computation invites new and original contributions with the understanding that, if accepted, they will not be published elsewhere. Papers reporting new theories, methodology, and/or important applications in quantum electronic structure, molecular dynamics, and statistical mechanics are appropriate for submission to this Journal. Specific topics include advances in or applications of ab initio quantum mechanics, density functional theory, design and properties of new materials, surface science, Monte Carlo simulations, solvation models, QM/MM calculations, biomolecular structure prediction, and molecular dynamics in the broadest sense including gas-phase dynamics, ab initio dynamics, biomolecular dynamics, and protein folding. The Journal does not consider papers that are straightforward applications of known methods including DFT and molecular dynamics. The Journal favors submissions that include advances in theory or methodology with applications to compelling problems.
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