Adaptive time steps in trajectory surface hopping simulations
Author(s) -
Lasse Spörkel,
Walter Thiel
Publication year - 2016
Publication title -
the journal of chemical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.071
H-Index - 357
eISSN - 1089-7690
pISSN - 0021-9606
DOI - 10.1063/1.4948956
Subject(s) - surface hopping , robustness (evolution) , atomic orbital , molecular dynamics , excited state , computer science , trajectory , statistical physics , control theory (sociology) , physics , simulation , chemistry , quantum mechanics , artificial intelligence , biochemistry , control (management) , gene , electron
Trajectory surface hopping (TSH) simulations are often performed in combination with active-space multi-reference configuration interaction (MRCI) treatments. Technical problems may arise in such simulations if active and inactive orbitals strongly mix and switch in some particular regions. We propose to use adaptive time steps when such regions are encountered in TSH simulations. For this purpose, we present a computational protocol that is easy to implement and increases the computational effort only in the critical regions. We test this procedure through TSH simulations of a GFP chromophore model (OHBI) and a light-driven rotary molecular motor (F-NAIBP) on semiempirical MRCI potential energy surfaces, by comparing the results from simulations with adaptive time steps to analogous ones with constant time steps. For both test molecules, the number of successful trajectories without technical failures rises significantly, from 53% to 95% for OHBI and from 25% to 96% for F-NAIBP. The computed excited-state lifetime remains essentially the same for OHBI and increases somewhat for F-NAIBP, and there is almost no change in the computed quantum efficiency for internal rotation in F-NAIBP. We recommend the general use of adaptive time steps in TSH simulations with active-space CI methods because this will help to avoid technical problems, increase the overall efficiency and robustness of the simulations, and allow for a more complete sampling
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