A mixed deterministic-stochastic algorithm of the branching corrected mean field method for nonadiabatic dynamics
We present a new algorithm of the branching corrected mean field (BCMF) method for nonadiabatic dynamics [J. Xu and L. Wang, J. Phys. Chem. Lett. 11, 8283 (2020)], which combines the key advantages of the two existed algorithms, i.e., the deterministic BCMF algorithm based on weights of trajectory b...
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| Veröffentlicht in: | The Journal of chemical physics Jg. 156; H. 11; S. 114116 |
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| Hauptverfasser: | , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
21.03.2022
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| ISSN: | 1089-7690, 1089-7690 |
| Online-Zugang: | Weitere Angaben |
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| Zusammenfassung: | We present a new algorithm of the branching corrected mean field (BCMF) method for nonadiabatic dynamics [J. Xu and L. Wang, J. Phys. Chem. Lett. 11, 8283 (2020)], which combines the key advantages of the two existed algorithms, i.e., the deterministic BCMF algorithm based on weights of trajectory branches (BCMF-w) and the stochastic BCMF algorithm with random collapse of the electronic wavefunction (BCMF-s). The resulting mixed deterministic-stochastic BCMF algorithm (BCMF-ws) is benchmarked in a series of standard scattering problems with potential wells on the excited-state surfaces, which are common in realistic systems. In all investigated cases, BCMF-ws holds the same high accuracy while the computational time is reduced about two orders of magnitude compared to the original BCMF-w and BCMF-s algorithms, thus promising for nonadiabatic dynamics simulations of general systems.We present a new algorithm of the branching corrected mean field (BCMF) method for nonadiabatic dynamics [J. Xu and L. Wang, J. Phys. Chem. Lett. 11, 8283 (2020)], which combines the key advantages of the two existed algorithms, i.e., the deterministic BCMF algorithm based on weights of trajectory branches (BCMF-w) and the stochastic BCMF algorithm with random collapse of the electronic wavefunction (BCMF-s). The resulting mixed deterministic-stochastic BCMF algorithm (BCMF-ws) is benchmarked in a series of standard scattering problems with potential wells on the excited-state surfaces, which are common in realistic systems. In all investigated cases, BCMF-ws holds the same high accuracy while the computational time is reduced about two orders of magnitude compared to the original BCMF-w and BCMF-s algorithms, thus promising for nonadiabatic dynamics simulations of general systems. |
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| Bibliographie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ISSN: | 1089-7690 1089-7690 |
| DOI: | 10.1063/5.0084013 |