Literature DB >> 35201520

NAST: Nonadiabatic Statistical Theory Package for Predicting Kinetics of Spin-Dependent Processes.

Vsevolod D Dergachev1, Mitra Rooein1, Ilya D Dergachev1, Aleksandr O Lykhin1,2, Robert C Mauban1, Sergey A Varganov3.   

Abstract

We present a nonadiabatic statistical theory (NAST) package for predicting kinetics of spin-dependent processes, such as intersystem crossings, spin-forbidden unimolecular reactions, and spin crossovers. The NAST package can calculate the probabilities and rates of transitions between the electronic states of different spin multiplicities. Both the microcanonical (energy-dependent) and canonical (temperature-dependent) rate constants can be obtained. Quantum effects, including tunneling, zero-point vibrational energy, and reaction path interference, can be accounted for. In the limit of an adiabatic unimolecular reaction proceeding on a single electronic state, NAST reduces to the traditional transition state theory. Because NAST requires molecular properties at only a few points on potential energy surfaces, it can be applied to large molecular systems, used with accurate high-level electronic structure methods, and employed to study slow nonadiabatic processes. The essential NAST input data include the nuclear Hessian at the reactant minimum, as well as the nuclear Hessians, energy gradients, and spin-orbit coupling at the minimum energy crossing point (MECP) between two states. The additional computational tools included in the NAST package can be used to extract the required input data from the output files of electronic structure packages, calculate the effective Hessian at the MECP, and fit the reaction coordinate for more advanced NAST calculations. We describe the theory, its implementation, and three examples of application to different molecular systems.
© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.

Entities:  

Keywords:  Intersystem crossings; Reaction rates; Spin-forbidden reactions; Spin–orbit coupling

Year:  2022        PMID: 35201520     DOI: 10.1007/s41061-022-00366-w

Source DB:  PubMed          Journal:  Top Curr Chem (Cham)        ISSN: 2364-8961


  36 in total

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