Erica C Mitchell, Justin M Turney, Henry F Schaefer
{"title":"Automatic Differentiation for Explicitly Correlated MP2.","authors":"Erica C Mitchell, Justin M Turney, Henry F Schaefer","doi":"10.1021/acs.jctc.4c00818","DOIUrl":null,"url":null,"abstract":"<p><p>Automatic differentiation (AD) offers a route to achieve arbitrary-order derivatives of challenging wave function methods without the use of analytic gradients or response theory. Currently, AD has been predominantly used in methods where first- and/or second-order derivatives are available, but it has not been applied to methods lacking available derivatives. The most robust approximation of explicitly correlated MP2, MP2-F12/3C(FIX)+CABS, is one such method. By comparing the results of MP2-F12 computed with AD versus finite-differences, it is shown that (a) optimized geometries match to about 10<sup>-3</sup> Å for bond lengths and a 10<sup>-6</sup> degree for angles, and (b) dipole moments match to about 10<sup>-6</sup> D. Hessians were observed to have poorer agreement with numerical results (10<sup>-5</sup>), which is attributed to deficiencies in AD implementations currently. However, it is notable that vibrational frequencies match within 10<sup>-2</sup> cm<sup>-1</sup>. The use of AD also allowed the prediction of MP2-F12/3C(FIX)+CABS IR intensities for the first time.</p>","PeriodicalId":45,"journal":{"name":"Journal of Chemical Theory and Computation","volume":null,"pages":null},"PeriodicalIF":5.7000,"publicationDate":"2024-09-23","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.4c00818","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
Automatic differentiation (AD) offers a route to achieve arbitrary-order derivatives of challenging wave function methods without the use of analytic gradients or response theory. Currently, AD has been predominantly used in methods where first- and/or second-order derivatives are available, but it has not been applied to methods lacking available derivatives. The most robust approximation of explicitly correlated MP2, MP2-F12/3C(FIX)+CABS, is one such method. By comparing the results of MP2-F12 computed with AD versus finite-differences, it is shown that (a) optimized geometries match to about 10-3 Å for bond lengths and a 10-6 degree for angles, and (b) dipole moments match to about 10-6 D. Hessians were observed to have poorer agreement with numerical results (10-5), which is attributed to deficiencies in AD implementations currently. However, it is notable that vibrational frequencies match within 10-2 cm-1. The use of AD also allowed the prediction of MP2-F12/3C(FIX)+CABS IR intensities for the first time.
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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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