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Data for 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) Data for Direct grid-based nonadiabatic dynamics on machine-learned potential energy surfaces : application to spin-forbidden processes. [Dataset]
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Abstract
We have recently shown how high-accuracy wavefunction 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 PES pre-fitting. To date, our approach has been demonstrated in the ground-state dynamics and non-adiabatic 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 non-adiabatic dynamics for spin-forbidden processes such as inter-system crossing (ISC), opening up new possibilities for modelling 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 modelling ISC in SO2 and thioformaldehyde, benchmarking our results against previous trajectory-based calculations. As a relatively efficient tool for modelling spin-forbidden non-adiabatic dynamics without demanding any pre-fitting of PESs, our overall strategy is a potentially powerful tool for modelling important photochemical systems, such as photoactivated pro-drugs and organometallic catalysts.
Item Type: | Dataset | ||||||||
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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 | ||||||||
Type of Data: | Experimental data | ||||||||
Library of Congress Subject Headings (LCSH): | Quantum chemistry, Quantum chemistry -- Computer programs, Gaussian processes, Machine learning, Kernel functions, Quantum theory | ||||||||
Publisher: | University of Warwick, Department of Chemistry | ||||||||
Official Date: | 6 October 2020 | ||||||||
Dates: |
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Status: | Not Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Media of Output (format): | .pdf .dat .dat~ .gnu .gnu~ .eps .eps~ .pl | ||||||||
Copyright Holders: | University of Warwick | ||||||||
Description: | Data record consists of a single zip archive, organised into directories, also containing an Icon file and an accompanying Readme file. |
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Date of first compliant deposit: | 26 October 2020 | ||||||||
Date of first compliant Open Access: | 26 October 2020 | ||||||||
RIOXX Funder/Project Grant: |
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