{"title":"O1NumHess: A Fast and Accurate Seminumerical Hessian Algorithm Using Only O(1) Gradients.","authors":"Bo Wang, Shaohang Luo, Zikuan Wang, Wenjian Liu","doi":"10.1021/acs.jctc.5c01354","DOIUrl":null,"url":null,"abstract":"<p><p>In this work, we describe a new algorithm, O1NumHess, to calculate the Hessian of a molecular system by finite differentiation of gradients calculated at <i>O</i>(1) displaced geometries, instead of <i>O</i>(<i>N</i><sub>atom</sub>) displaced geometries (where <i>N</i><sub>atom</sub> 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 <i>O</i>(<i>N</i><sub>atom</sub><sup>2</sup>) to <i>O</i>(<i>N</i><sub>atom</sub>), such that <i>O</i>(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.</p>","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":" ","pages":""},"PeriodicalIF":5.5000,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Chemical Theory and Computation","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1021/acs.jctc.5c01354","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
引用次数: 0
Abstract
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.
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.