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A mixed deterministic-stochastic algorithm of the branching corrected mean field method for nonadiabatic dynamics
Bing Li1, Jiabo Xu1, Guijie Li1
1Key Laboratory of Excited-State Materials of Zhejiang Province, Department of Chemistry, Zhejiang University, Hangzhou 310027, China.
A new mixed deterministic-stochastic algorithm (BCMF-ws) enhances nonadiabatic dynamics simulations. This method achieves high accuracy with significantly reduced computational time, making it promising for complex systems.
Area of Science:
- Chemical Physics
- Computational Chemistry
- Quantum Dynamics
Background:
- Nonadiabatic dynamics simulations are crucial for understanding chemical reactions.
- Existing methods like BCMF-w and BCMF-s have limitations in accuracy or computational cost.
Purpose of the Study:
- To develop a novel algorithm for nonadiabatic dynamics.
- To combine the strengths of deterministic and stochastic BCMF methods.
Main Methods:
- Introducing the mixed deterministic-stochastic BCMF algorithm (BCMF-ws).
- Benchmarking BCMF-ws using scattering problems with potential wells on excited-state surfaces.
Main Results:
- BCMF-ws demonstrates high accuracy comparable to existing methods.
- The new algorithm reduces computational time by approximately two orders of magnitude.
Conclusions:
- BCMF-ws offers a significant improvement in efficiency for nonadiabatic dynamics.
- The method is well-suited for simulating complex chemical systems.
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