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 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.& nbsp;Published under an exclusive license by AIP Publishing

Automated summary

This paper presents a new algorithm of the branching corrected mean field (BCMF) method for nonadiabatic dynamics. The algorithm combines the advantages of two existing algorithms - the deterministic BCMF algorithm and the stochastic BCMF algorithm. The resulting mixed deterministic-stochastic BCMF algorithm is benchmarked and shows high accuracy with significantly reduced computational time compared to the original algorithms, making it promising for nonadiabatic dynamics simulations of general systems.