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- W3094352124 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." @default.
- W3094352124 created "2020-10-29" @default.
- W3094352124 creator A5029969579 @default.
- W3094352124 creator A5054262500 @default.
- W3094352124 date "2020-10-26" @default.
- W3094352124 modified "2023-09-26" @default.
- W3094352124 title "Direct Grid-Based Nonadiabatic Dynamics on Machine-Learned Potential Energy Surfaces: Application to Spin-Forbidden Processes" @default.
- W3094352124 cites W1498377335 @default.
- W3094352124 cites W1969087374 @default.
- W3094352124 cites W1971362449 @default.
- W3094352124 cites W1973339645 @default.
- W3094352124 cites W1973385516 @default.
- W3094352124 cites W1981537062 @default.
- W3094352124 cites W1982042866 @default.
- W3094352124 cites W1987961008 @default.
- W3094352124 cites W1988996307 @default.
- W3094352124 cites W1991975359 @default.
- W3094352124 cites W1993608956 @default.
- W3094352124 cites W1995308330 @default.
- W3094352124 cites W2005912747 @default.
- W3094352124 cites W2007476501 @default.
- W3094352124 cites W2014209007 @default.
- W3094352124 cites W2016352498 @default.
- W3094352124 cites W2018605367 @default.
- W3094352124 cites W2020017746 @default.
- W3094352124 cites W2029173015 @default.
- W3094352124 cites W2030380628 @default.
- W3094352124 cites W2038922877 @default.
- W3094352124 cites W2040209564 @default.
- W3094352124 cites W2040654649 @default.
- W3094352124 cites W2042951945 @default.
- W3094352124 cites W2043453635 @default.
- W3094352124 cites W2043767213 @default.
- W3094352124 cites W2053815810 @default.
- W3094352124 cites W2054601803 @default.
- W3094352124 cites W2059104184 @default.
- W3094352124 cites W2061863318 @default.
- W3094352124 cites W2065118482 @default.
- W3094352124 cites W2080081205 @default.
- W3094352124 cites W2080194069 @default.
- W3094352124 cites W2081604033 @default.
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- W3094352124 cites W2161062839 @default.
- W3094352124 cites W2166167776 @default.
- W3094352124 cites W2168051561 @default.
- W3094352124 cites W2295188415 @default.
- W3094352124 cites W2316626263 @default.
- W3094352124 cites W2332703524 @default.
- W3094352124 cites W2340440493 @default.
- W3094352124 cites W2416641138 @default.
- W3094352124 cites W2491218394 @default.
- W3094352124 cites W2582543345 @default.
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- W3094352124 cites W2905224208 @default.
- W3094352124 cites W2907561373 @default.
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- W3094352124 doi "https://doi.org/10.1021/acs.jpca.0c06125" @default.
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