Clarification of nonadiabatic chemical dynamics by the Zhu-Nakamura theory of nonadiabatic transition: from tri-atomic systems to reactions in solutions

Ishida, Toshimasa; Nanbu, Shinkoh; Nakamura, Hiroki

doi:10.1080/0144235x.2017.1293399

Cited by 30 publications

(37 citation statements)

References 168 publications

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“…In the field of SH dynamics targeted at the simulation of ISC processes, one can distinguish between three general approaches. The oldest one combines regular Tully SH with ISC probabilities computed only at the relevant crossing points, using transition probabilities from Landau-Zener (Zener, 1932) or Zhu-Nakamura (Ishida, Nanbu, & Nakamura, 2017) theory to estimate the singlet-to-triplet transition probability. Such procedure has been used for different kinds of collision reactions involving ISC (Abrahamsson, Andersson, Marković, & Nyman, 2009;Amatatsu, Morokuma, & Yabushita, 1991;Hu, Lendvay, Maiti, & Schatz, 2008;Rajak & Maiti, 2014;Tachikawa, Ohnishi, Hamabayashi, & Yoshida, 1997;Yamashita & Morokuma, 1990).…”

Section: Alternative Approachesmentioning

confidence: 99%

Nonadiabatic dynamics: The SHARC approach

Mai

Marquetand

González

2018

WIREs Comput Mol Sci

402

493

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We review the Surface Hopping including ARbitrary Couplings (SHARC) approach for excited‐state nonadiabatic dynamics simulations. As a generalization of the popular surface hopping method, SHARC allows simulating the full‐dimensional dynamics of molecules including any type of coupling terms beyond nonadiabatic couplings. Examples of these arbitrary couplings include spin–orbit couplings or dipole moment–laser field couplings, such that SHARC can describe ultrafast internal conversion, intersystem crossing, and radiative processes. The key step of the SHARC approach consists of a diagonalization of the Hamiltonian including these couplings, such that the nuclear dynamics is carried out on potential energy surfaces including the effects of the couplings—this is critical in any applications considering, for example, transition metal complexes or strong laser fields. We also give an overview over the new SHARC2.0 dynamics software package, released under the GNU General Public License, which implements the SHARC approach and several analysis tools. The review closes with a brief survey of applications where SHARC was employed to study the nonadiabatic dynamics of a wide range of molecular systems.This article is categorized under: Theoretical and Physical Chemistry > Reaction Dynamics and KineticsSoftware > Simulation MethodsSoftware > Quantum Chemistry

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Section: Alternative Approachesmentioning

confidence: 99%

Nonadiabatic dynamics: The SHARC approach

Mai

Marquetand

González

2018

WIREs Comput Mol Sci

402

493

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“…Because the predicted interstate terms of NACs have relatively larger errors than energies and forces, the behavior of NN surface hopping events can be very different from them in QC trajectories. We compared the FSSH and Zhu-Nakamura theory of surface hopping (ZNSH) [41][42][43][44][45] to determine the NACs inuence on the NN trajectories. The ZNSH method is independent on NACs that has been successfully applied to excited-state dynamics of other systems 46,47 and used to train machine learning trajectories.…”

Section: Ml-namd Simulations For the Cis-trans Isomerization Of Trans-hexauoro-2-butenementioning

confidence: 99%

Automatic discovery of photoisomerization mechanisms with nanosecond machine learning photodynamics simulations

Reiser

Boswell

et al. 2021

Chem. Sci.

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“…Very similar to this approach is the Zhu–Nakamura theory. 442 − 445 Also here, the computation of couplings is omitted, and only information about PESs is used. Among the mostly used hopping algorithm is Tully’s fewest switches algorithm, 436 which is valid for many cases and based on the NACs between different PESs.…”

Section: Quantum Chemical Theory and Methodsmentioning

confidence: 99%

Machine Learning for Electronically Excited States of Molecules

2020

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Electronically excited states of molecules are at the heart of photochemistry, photophysics, as well as photobiology and also play a role in material science. Their theoretical description requires highly accurate quantum chemical calculations, which are computationally expensive. In this review, we focus on not only how machine learning is employed to speed up such excited-state simulations but also how this branch of artificial intelligence can be used to advance this exciting research field in all its aspects. Discussed applications of machine learning for excited states include excited-state dynamics simulations, static calculations of absorption spectra, as well as many others. In order to put these studies into context, we discuss the promises and pitfalls of the involved machine learning techniques. Since the latter are mostly based on quantum chemistry calculations, we also provide a short introduction into excited-state electronic structure methods and approaches for nonadiabatic dynamics simulations and describe tricks and problems when using them in machine learning for excited states of molecules.

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Clarification of nonadiabatic chemical dynamics by the Zhu-Nakamura theory of nonadiabatic transition: from tri-atomic systems to reactions in solutions

Cited by 30 publications

References 168 publications

Nonadiabatic dynamics: The SHARC approach

Nonadiabatic dynamics: The SHARC approach

Automatic discovery of photoisomerization mechanisms with nanosecond machine learning photodynamics simulations

Machine Learning for Electronically Excited States of Molecules

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