Nonadiabatic dynamics: The SHARC approach

Mai, Sebastian; Marquetand, Philipp; González, Leticia

doi:10.1002/wcms.1370

Cited by 386 publications

(534 citation statements)

References 184 publications

(290 reference statements)

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“…Even though the seminal paper by Tully on the fewest switches surface hopping (FSSH) algorithm was published almost 30 years ago this method is still in active use, due to its low computational expense and its relatively simple implementation. In the chemical community, this technique has become routinely available through its implementation in software packages such as Newton‐X, Fish, or Sharc providing a combination of SHT techniques with standard electronic structure software packages. In those approaches, the forces and the nonadiabatic couplings (NACs) governing the trajectories are computed on‐the‐fly by means of ab initio or semi‐empirical electronic structure calculations.…”

Section: Introductionmentioning

confidence: 99%

WavePacket: A Matlab package for numerical quantum dynamics. III. Quantum‐classical simulations and surface hopping trajectories

2019

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WavePacket is an open‐source program package for numerical simulations in quantum dynamics. Building on the previous Part I (Schmidt and Lorenz, Comput. Phys. Commun. 2017, 213, 223] and Part II (Schmidt and Hartmann, Comput. Phys. Commun. 2018, 228, 229] which dealt with quantum dynamics of closed and open systems, respectively, the present Part III adds fully classical and mixed quantum‐classical propagation techniques to WavePacket. There classical phase‐space densities are sampled by trajectories which follow (diabatic or adiabatic) potential energy surfaces. In the vicinity of (genuine or avoided) intersections of those surfaces, trajectories may switch between them. To model these transitions, two classes of stochastic algorithms have been implemented: (1) Tully's fewest switches surface hopping and (2) Landau–Zener‐based single switch surface hopping. The latter one offers the advantage of being based on adiabatic energy gaps only, thus not requiring nonadiabatic coupling information any more. The present work describes the MATLAB version of WavePacket 6.1.0, which is essentially an object‐oriented rewrite of previous versions, allowing to perform fully classical, quantum‐classical and quantum‐mechanical simulations on an equal footing, that is, for the same physical system described by the same WavePacket input. The software package is hosted and further developed at the Sourceforge platform, where also extensive Wiki‐documentation as well as numerous worked‐out demonstration examples with animated graphics are available. © 2019 Wiley Periodicals, Inc.

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Section: Introductionmentioning

confidence: 99%

WavePacket: A Matlab package for numerical quantum dynamics. III. Quantum‐classical simulations and surface hopping trajectories

2019

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“…In order to compute nonadiabatic molecular dynamics simulations, the SHARC [36][37][38] method, an extension of Tully's fewest switches algorithm [35], is applied. This mixed quantum-classical approach allows for on-the-fly computation of the PESs with electronic structure methods, on which the nuclei move according to Newton's second equation of motion.…”

Section: Surface Hopping Molecular Dynamicsmentioning

confidence: 99%

“…A popular extension for this method including not only nonadiabatic couplings (NACs) but also other couplings, e.g., spin-orbit couplings (SOCs), is the SHARC (surface hopping including arbitrary couplings) approach [36][37][38]. Importantly, NACs, also called derivative couplings, are used to determine the hopping directions and probabilities between states of same spin multiplicity [36,37,[39][40][41]. The NAC vector (denoted as C NAC ) between two states, i and j, can be computed as [39,42,43] where the second-order derivatives are neglected.…”

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confidence: 99%

“…SOCs (denoted as C SOC ) are present between states of different spin multiplicity,and determine the rate of intersystem crossing. They are obtained as off-diagonal elements of the Hamiltonian matrix in standard electronic-structure calculations [37,53].Most of the recent studies involving ML dynamics deal with ground-state MD simulations, see e.g. Refs.…”

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confidence: 99%

“…and determine the rate of intersystem crossing. They are obtained as off-diagonal elements of the Hamiltonian matrix in standard electronic-structure calculations [37,53].…”

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confidence: 99%

See 2 more Smart Citations

Combining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State Dynamics

Westermayr

Gastegger

Marquetand

2020

J. Phys. Chem. Lett.

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167

298

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In recent years, deep learning has become a part of our everyday life and is revolutionizing quantum chemistry as well. In this work, we show how deep learning can be used to advance the research field of photochemistry by learning all important properties for photodynamics simulations. The properties are multiple energies, forces, nonadiabatic couplings and spin-orbit couplings. The nonadiabatic couplings are learned in a phasefree manner as derivatives of a virtually constructed property by the deep learning model, which guarantees rotational covariance. Additionally, an approximation for nonadiabatic couplings is introduced, based on the potentials, their gradients and Hessians. As deep-learning method, we employ SchNet extended for multiple electronic states. In combination with the molecular dynamics program SHARC, our approach termed SchNarc is tested on a model system and two realistic polyatomic molecules and paves the way towards efficient photodynamics simulations of complex systems.1 Excited-state dynamics simulations are powerful tools to predict, understand and explain photo-induced processes, especially in combination with experimental studies. Examples of photo-induced processes range from photosynthesis, DNA photodamage as the starting point of skin cancer, to processes that enable our vision [1][2][3][4][5]. As they are part of our everyday lives, their understanding can help to unravel fundamental processes of nature and to advance several research fields, such as photovoltaics [6,7], photocatalysis [8] or photosensitive drug design [9].Since the full quantum mechanical treatment of molecules remains challenging, exact quantum dynamics simulations are limited to systems containing only a couple of atoms, even if fitted potential energy surfaces (PESs) are used [10][11][12][13][14][15][16][16][17][18][19][20][21][22][23][24][25][26]. In order to treat larger systems in full dimensions, i.e., systems with up to 100s of atoms, and on long time scales, i.e., in the range of several 100 picoseconds, excited-state machine learning (ML) molecular dynamics (MD), where the ML model is trained on quantum chemistry data, has evolved as a promising tool in the last couple of years [27][28][29][30][31][32][33].Such nonadiabatic MLMD simulations are in many senses analog to excited-state ab initio molecular dynamics simulations. The only difference is that the costly electronic structure calculations are mostly replaced by a ML model, providing quantum properties like the PESs and the corresponding forces. The nuclei are assumed to move classically on those PESs. This mixed quantum-classical dynamics approach allows for a very fast on-the-fly evaluation of the necessary properties at the geometries visited during the dynamics simulations.In order to account for nonadiabatic effects, i.e., transitions from one state to another, further approximations have to be introduced [34]. One method, which is frequently used to account for such transitions, is the surface-hopping method originally developed by Tully [35]...

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