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Direct grid-based nonadiabatic dynamics on machine-learned potential energy surfaces : application to spin-forbidden processes
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Richings, Gareth W. and Habershon, Scott (2020) Direct grid-based nonadiabatic dynamics on machine-learned potential energy surfaces : application to spin-forbidden processes. Journal of Physical Chemistry A, 124 (44). pp. 9299-9313. doi:10.1021/acs.jpca.0c06125 ISSN 1089-5639.
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WRAP-Direct-grid-based-non-adiabatic-dynamics-machine-potential-energy-spin-2020.pdf - Accepted Version - Requires a PDF viewer. Download (1157Kb) | Preview |
Official URL: https://doi.org/10.1021/acs.jpca.0c06125
Abstract
We have recently shown how high-accuracy wave function grid-based propagation schemes, such as the multiconfiguration time-dependent Hartree (MCTDH) method, can be combined with machine-learning (ML) descriptions of PESs to yield an “on-the-fly” direct dynamics scheme which circumvents potential energy surface (PES) prefitting. To date, our approach has been demonstrated in the ground-state dynamics and nonadiabatic spin-allowed dynamics of several molecular systems. Expanding on this successful previous work, this Article demonstrates how our ML-based quantum dynamics scheme can be adapted to model nonadiabatic dynamics for spin-forbidden processes such as intersystem crossing (ISC), opening up new possibilities for modeling chemical dynamic phenomena driven by spin–orbit coupling. After describing modifications to diabatization schemes to enable accurate and robust treatment or electronic states of different spin-multiplicity, we demonstrate our methodology in applications to modeling ISC in SO2 and thioformaldehyde, benchmarking our results against previous trajectory- and grid-based calculations. As a relatively efficient tool for modeling spin-forbidden nonadiabatic dynamics without demanding any prefitting of PESs, our overall strategy is a potentially powerful tool for modeling important photochemical systems, such as photoactivated pro-drugs and organometallic catalysts.
Item Type: | Journal Article | ||||||||
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Subjects: | Q Science > QA Mathematics Q Science > QD Chemistry |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Chemistry | ||||||||
Library of Congress Subject Headings (LCSH): | Quantum chemistry, Quantum chemistry -- Computer programs , Gaussian processes , Machine learning, Kernel functions, Quantum theory | ||||||||
Journal or Publication Title: | Journal of Physical Chemistry A | ||||||||
Publisher: | American Chemical Society | ||||||||
ISSN: | 1089-5639 | ||||||||
Official Date: | 5 November 2020 | ||||||||
Dates: |
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Volume: | 124 | ||||||||
Number: | 44 | ||||||||
Page Range: | pp. 9299-9313 | ||||||||
DOI: | 10.1021/acs.jpca.0c06125 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Reuse Statement (publisher, data, author rights): | “This document is the Accepted Manuscript version of a Published Work that appeared in final form in Journal of Physical Chemistry A, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see [insert ACS Articles on Request author-directed link to Published Work, see http://pubs.acs.org/page/policy/articlesonrequest/index.html].” | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Copyright Holders: | Copyright © 2020 American Chemical Society | ||||||||
Date of first compliant deposit: | 25 March 2021 | ||||||||
Date of first compliant Open Access: | 26 October 2021 | ||||||||
RIOXX Funder/Project Grant: |
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