Pedagogical Overview of the Fewest Switches Surface Hopping Method

Jain, Amber; Sindhu, Aarti

doi:10.1021/acsomega.2c04843

“…The most efficient methods for simulating electronically nonadiabatic processes are semiclassical methods, in which the electronic structure is modeled quantum mechanically, and nuclear motion follows a semiclassical trajectory. − ,− We consider calculations in the adiabatic representation where the coupling of electronic states is due to the nonadiabatic coupling vector (NAC). We focus first on the coherent propagation of electronic coefficients, but decoherence will be added below.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Nonadiabatic Coupling in Trajectory Surface Hopping: Accurate Time Derivative Couplings by the Curvature-Driven Approximation

Zhao,

Merritt,

Lei

et al. 2023

J. Chem. Theory Comput.

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Trajectory surface hopping (TSH) is a widely used mixed quantum-classical dynamics method that is used to simulate molecular dynamics with multiple electronic states. In TSH, timederivative coupling is employed to propagate the electronic coefficients and in that way to determine when the electronic state on which the nuclear trajectory is propagated switches. In this work, we discuss nonadiabatic TSH dynamics algorithms employing the curvature-driven approximation and overlap-based time derivative couplings, and we report test calculations on six photochemical reactions where we compare the results to one another and to calculations employing analytic nonadiabatic coupling vectors. We correct previous published results thanks to a bug found in the software. We also provide additional, more detailed studies of the time-derivative couplings. Our results show good agreement between curvature-driven algorithms and overlap-based algorithms.

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“…Thus, many approximations are adopted such as averaging out environment degrees of freedom 18 and classical description of the system and/or environment. 19,20 In the past three decades, great progress has been made in the development of theoretical approaches for open quantum systems. These approaches include the perturbative Redfield equation, 21 the exact Nakajima-Zwanzig formalism 22,23 and its kernel-based expansions, [24][25][26] the quantum-classical and fully classical approaches, 20,[27][28][29][30] Green's function formalism, 31 the transfer tensor method 32 and its extension, 33 the multi-configuration time-dependent Hartree (MCTDH), 34,35 the pseudo-mode approach, 36,37 the reaction coordinate (RC) approach, 38,39 the quantum Monte Carlo (QMC), 40,41 the time-dependent numerical renormalization group (NRG), 42 the density matrix renormalization group (tDMRG), 43 the polaron transformation, 24,44 the time evolving density matrix using the orthogonal polynomial algorithm (TEDOPA), 45,46 the quasiadiabatic propagator path integral (QuAPI), 47,48 the numerical variational method (NVM), 49 the automated compression of environment (ACE) method, 50 the hierarchy equations of motion (HEOM), [51][52][53][54]…”

Section: Introductionmentioning

confidence: 99%

MLQD: A package for machine learning-based quantum dissipative dynamics

Ullah

¹

,

Dral

²

2023

Preprint

0

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Machine learning has emerged as a promising paradigm to study the quantum dissipative dynamics of open quantum systems. To facilitate the use of our recently published ML-based approaches for quantum dissipative dynamics, here we present an open-source Python package MLQD (https://github.com/Arif-PhyChem/MLQD), which currently supports the three ML-based quantum dynamics approaches: (1) the recursive dynamics with kernel ridge regression (KRR) method, (2) the non-recursive artificial-intelligence-based quantum dynamics (AIQD) approach and (3) the blazingly fast one-shot trajectory learning (OSTL) approach, where both AIQD and OSTL use the convolutional neural networks (CNN). This paper describes the features of the MLQD package, the technical details, optimization of hyperparameters, visualization of results, and the demonstration of the MLQD's applicability for two widely studied systems, namely the spin-boson model and the Fenna--Matthews--Olson (FMO) complex. To make MLQD more user-friendly and accessible, we have made it available on the XACS cloud computing platform (https://XACScloud.com) via the interface to the MLatom package (http://MLatom.com).

show abstract

“…Thus, many approximations are adopted such as averaging out environment degrees of freedom 18 and classical description of the system and/or environment. 19,20 In the past three decades, great progress has been made in the development of theoretical approaches for open quantum systems. These approaches include the perturbative Redfield equation, 21 the exact Nakajima-Zwanzig formalism 22,23 and its kernel-based expansions, [24][25][26] the quantum-classical and fully classical approaches, 20,[27][28][29][30] Green's function formalism, 31 the transfer tensor method 32 and its extension, 33 the multi-configuration time-dependent Hartree (MCTDH), 34,35 the pseudo-mode approach, 36,37 the reaction coordinate (RC) approach, 38,39 the quantum Monte Carlo (QMC), 40,41 the time-dependent numerical renormalization group (NRG), 42 the density matrix renormalization group (tDMRG), 43 the polaron transformation, 24,44 the time evolving density matrix using the orthogonal polynomial algorithm (TEDOPA), 45,46 the quasiadiabatic propagator path integral (QuAPI), 47,48 the numerical variational method (NVM), 49 the automated compression of environment (ACE) method, 50 the hierarchy equations of motion (HEOM), [51][52][53][54]…”

Section: Introductionmentioning

confidence: 99%

MLQD: A package for machine learning-based quantum dissipative dynamics

Ullah

¹

,

Dral

²

2023

Preprint

0

View full text Add to dashboard Cite

Machine learning has emerged as a promising paradigm to study the quantum dissipative dynamics of open quantum systems. To facilitate the use of our recently published ML-based approaches for quantum dissipative dynamics, here we present an open-source Python package MLQD (https://github.com/Arif-PhyChem/MLQD), which currently supports the three ML-based quantum dynamics approaches: (1) the recursive dynamics with kernel ridge regression (KRR) method, (2) the non-recursive artificial-intelligence-based quantum dynamics (AIQD) approach and (3) the blazingly fast one-shot trajectory learning (OSTL) approach, where both AIQD and OSTL use the convolutional neural networks (CNN). This paper describes the features of the MLQD package, the technical details, optimization of hyperparameters, visualization of results, and the demonstration of the MLQD's applicability for two widely studied systems, namely the spin-boson model and the Fenna--Matthews--Olson (FMO) complex. To make MLQD more user-friendly and accessible, we have made it available on the Python Package Index (PyPi) platform and it can be installed via pip install mlqd. In addition, it is also available on the XACS cloud computing platform (https://XACScloud.com) via the interface to the MLATOM package (http://MLatom.com)

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Pedagogical Overview of the Fewest Switches Surface Hopping Method

Cited by 30 publications

References 74 publications

Nonadiabatic Coupling in Trajectory Surface Hopping: Accurate Time Derivative Couplings by the Curvature-Driven Approximation

Nonadiabatic Coupling in Trajectory Surface Hopping: Accurate Time Derivative Couplings by the Curvature-Driven Approximation

MLQD: A package for machine learning-based quantum dissipative dynamics

MLQD: A package for machine learning-based quantum dissipative dynamics

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