Learning Optimal Decoherence Time Formulas for Surface Hopping Simulation of High-Dimensional Scattering
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| Format: | Preprint |
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2025
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| _version_ | 1866911226384089088 |
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| author | Shao, Cancan Xie, Rixin Shi, Zhecun Wang, Linjun |
| author_facet | Shao, Cancan Xie, Rixin Shi, Zhecun Wang, Linjun |
| contents | In our recent work (J. Phys. Chem. Lett. 2023, 14, 7680), we utilized the exact quantum dynamics results as references and proposed a general machine learning method to obtain the optimal decoherence time formula for surface hopping simulation. Here, we extend this strategy from one-dimensional systems to the much more intricate scenarios with multiple nuclear dimensions. Different from the one-dimensional situation, an effective nuclear kinetic energy is defined by extracting the component of nuclear momenta along the non-adiabatic coupling vector. Combined with the energy difference between adiabatic states, high-order descriptor space can be generated by binary operations. Then the optimal decoherence time formula can be obtained by machine learning procedures based on the full quantum dynamics reference data. Although we only use the final channel populations in 24 scattering samples as training data for machine learning, the obtained optimal decoherence time formula can well reproduce the time evolution of the reduced and spatial distribution of population. As benchmarked in a large number of 56840 one- and two-dimensional samples, the optimal decoherence time formula shows exceptionally high and uniform performance when compared with all other available formulas. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_19238 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Optimal Decoherence Time Formulas for Surface Hopping Simulation of High-Dimensional Scattering Shao, Cancan Xie, Rixin Shi, Zhecun Wang, Linjun Chemical Physics In our recent work (J. Phys. Chem. Lett. 2023, 14, 7680), we utilized the exact quantum dynamics results as references and proposed a general machine learning method to obtain the optimal decoherence time formula for surface hopping simulation. Here, we extend this strategy from one-dimensional systems to the much more intricate scenarios with multiple nuclear dimensions. Different from the one-dimensional situation, an effective nuclear kinetic energy is defined by extracting the component of nuclear momenta along the non-adiabatic coupling vector. Combined with the energy difference between adiabatic states, high-order descriptor space can be generated by binary operations. Then the optimal decoherence time formula can be obtained by machine learning procedures based on the full quantum dynamics reference data. Although we only use the final channel populations in 24 scattering samples as training data for machine learning, the obtained optimal decoherence time formula can well reproduce the time evolution of the reduced and spatial distribution of population. As benchmarked in a large number of 56840 one- and two-dimensional samples, the optimal decoherence time formula shows exceptionally high and uniform performance when compared with all other available formulas. |
| title | Learning Optimal Decoherence Time Formulas for Surface Hopping Simulation of High-Dimensional Scattering |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2510.19238 |