Ab initio multiple-spawning (AIMS) describes the
nonadiabatic dynamics of molecules by expanding nuclear wave functions
in a basis of traveling multidimensional Gaussians called trajectory
basis functions (TBFs). New TBFs can be spawned whenever nuclear amplitude
is transferred between electronic states due to nonadiabatic transitions.
While the adaptive size of the TBF basis grants AIMS its characteristic
accuracy in describing nonadiabatic processes, it also leads to a
fast and uncontrolled growth of the number of TBFs, penalizing computational
efficiency. A different flavor of AIMS, called AIMS with informed
stochastic selections (AIMSWISS), has recently been proposed to reduce
the number of TBFs dramatically. Herein, we test the performance of
AIMSWISS for a series of challenging nonadiabatic processesphotodynamics
of two-dimensional model systems, 1,2-dithiane and chromium (0) hexacarbonyland
show that this method is robust and extends the range of molecular
systems that can be simulated within the multiple-spawning framework.
Ab initio multiple spawning (AIMS) offers a reliable strategy to describe the excited-state dynamics and nonadiabatic processes of molecular systems. AIMS represents nuclear wavefunctions as linear combinations of traveling, coupled Gaussians called trajectory basis functions (TBFs) and uses a spawning algorithm to increase as needed the size of this basis set during nonadiabatic transitions. While the success of AIMS resides in this spawning algorithm, the dramatic increase in TBFs generated by multiple crossings between electronic states can rapidly lead to intractable dynamics. In this Communication, we introduce a new flavor of AIMS, coined ab initio multiple spawning with informed stochastic selections (AIMSWISS), which proposes a parameter-free strategy to beat the growing number of TBFs in an AIMS dynamics while preserving its accurate description of nonadiabatic transitions. The performance of AIMSWISS is validated against the photodynamics of ethylene, cyclopropanone, and fulvene. This technique, built upon the recently developed stochastic-selection AIMS, is intended to serve as a computationally affordable starting point for multiple spawning simulations.
Full multiple spawning (FMS) offers a strategy to simulate the nonadiabatic dynamics of molecular systems by describing their nuclear wavefunctions by a linear combination of coupled trajectory basis functions (TBFs). Applying a series of controlled approximations to the full multiple spawning (FMS) equations leads to the ab initio multiple spawning (AIMS), which is compatible with an on-the-fly propagation of the TBFs and an accurate description of nonadiabatic processes. The AIMS strategy and its numerical implementations, however, rely on a series of user-defined parameters. Herein, we investigate the influence of these parameters on the electronic-state population of two molecular systems— trans-azomethane and a two-dimensional model of the butatriene cation. This work highlights the stability of AIMS with respect to most of its parameters, underlines the specific parameters that require particular attention from the user of the method, and offers prescriptions for an informed selection of their value.
Full multiple spawning (FMS) offers a strategy to simulate the nonadiabatic dynamics of molecular systems by describing their nuclear wavefunctions by a linear combination of coupled trajectory basis functions (TBFs). Applying a series of controlled approximations to the FMS equations leads to the ab initio multiple spawning (AIMS), which is compatible with an on-the-fly propagation of the TBFs and an accurate description of nonadiabatic processes. The AIMS strategy and its numerical implementations, however, rely on a series of user-defined parameters. Herein, we investigate the influence of these parameters on the electronic-state population of two molecular systems – trans-azomethane and a two-dimensional model of the butatriene cation. This work highlights the stability of AIMS with respect to most of its parameters, underlines the specific parameters that require particular attention from the user of the method, and offers prescriptions for an informed selection of their value.
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