Benchmarking Quasiclassical Mapping Hamiltonian Methods for Simulating Cavity-Modified Molecular Dynamics

Saller, Maximilian A. C.; Kelly, Aaron; Geva, Eitan

doi:10.1021/acs.jpclett.1c00158

Cited by 23 publications

(35 citation statements)

References 94 publications

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“…62,63 . 50,62,63 . In contrast, Ehrenfest dynamics yields reasonable behavior at very short times in the previous benchmark models studied in the paper, although its long time performance is often poor.…”

Section: Atom-in-cavity Modelsmentioning

confidence: 99%

“…the Ehrenfest dynamics results considerably deviate from the exact population dynamics even at very short times, which agrees with what was reported in Refs. 50,62,63 . In contrast, Ehrenfest dynamics yields reasonable behavior at very short times in the previous benchmark models studied in the paper, although its long time performance is often poor.…”

Section: Atom-in-cavity Modelsmentioning

confidence: 99%

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Commutator Matrix in Phase Space Mapping Models for Nonadiabatic Quantum Dynamics

Gong

et al. 2021

J. Phys. Chem. A

107

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We show that a novel, general phase space mapping Hamiltonian for nonadiabatic systems, which is reminiscent of the renowned Meyer-Miller mapping Hamiltonian, involves a commutator variable matrix rather than the conventional zero-point-energy parameter. In the exact mapping formulation on constraint space for phase space approaches for nonadiabatic dynamics, the general mapping Hamiltonian with commutator variables can be employed to generate approximate trajectory-based dynamics. Various benchmark model tests, which range from gas phase to condensed phase systems, suggest that the overall performance of the general mapping Hamiltonian is better than that of the conventional Meyer-Miller Hamiltonian.

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Section: Atom-in-cavity Modelsmentioning

confidence: 99%

Section: Atom-in-cavity Modelsmentioning

confidence: 99%

Commutator Matrix in Phase Space Mapping Models for Nonadiabatic Quantum Dynamics

Gong

et al. 2021

J. Phys. Chem. A

107

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“…1/2 for different electronic operators and whether the resolution of identity relation is used. Here, we focus on the RI-LSC1 approach, which is also known as mLSC/φ 1 φ 1 in literature: 15,[51][52][53][54] mapping No. 1 is used for quantities at time 0 and mapping No.…”

Section: B Linearized Semiclassical Mapping Dynamicsmentioning

confidence: 99%

Forecasting nonadiabatic dynamics using hybrid convolutional neural network/long short-term memory network

et al. 2021

The Journal of Chemical Physics

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Modeling nonadiabatic dynamics in complex molecular or condensed-phase systems has been challenging especially for the long-time dynamics. In this work, we propose a time series machine learning scheme based on the hybrid convolutional neural network/long short-term memory (CNN-LSTM) framework for predicting the long-time quantum behavior given only the short-time dynamics. This scheme takes advantage of both the powerful local feature extraction ability of CNN and the long-term global sequential pattern recognition ability of LSTM. With feature fusion of individually trained CNN-LSTM models for the quantum population and coherence dynamics, the proposed scheme is shown to have high accuracy and robustness in predicting the linearized semiclassical and symmetrical quasiclassical mapping dynamics of various spin-boson models with learning time up to 0.3 ps. Furthermore, if the hybrid network has learned the dynamics of a system, this knowledge is transferable that could significantly enhance the accuracy in predicting the dynamics of a similar system. The hybrid CNN-LSTM network is thus believed to have high predictive power in forecasting the nonadiabatic dynamics in realistic charge and energy transfer processes in photoinduced energy conversion.

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“…This methodology for sampling the electronic variables and evaluating the population follows the work of Chowdhury and Huo, 37 though more recently an alternative form has been presented 38 that more closely matches previous work with Meyer-Miller mapping dynamics. 32,34 When using only a single bead for NRPMD, the method becomes equivalent to the LSCI of Gao et al 32 with focused initial conditions. 78…”

Section: Nonadiabatic Ring Polymer Molecular Dynamics (Nrpmd)mentioning

confidence: 99%

“…Examples include: Ehrenfest dynamics, [7][8][9] molecular dynamics with surface hopping, [10][11][12][13][14][15][16] mixed quantum-classical Liouville dynamics, [17][18][19][20] the quantum-classical path integral method, 21,22 and semiclassical mapping Hamiltonian methods. [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38] Most of these methods were conceived with a relatively small number of electronic states in mind, but some have been extended and modified to tackle the continuum of states encountered in metallic environments. In particular, these include surface hopping methods, [39][40][41][42] molecular dynamics with electronic friction, [43][44][45][46][47] and mapping variable techniques.…”

Section: Introductionmentioning

confidence: 99%

NQCDynamics.jl: A Julia Package for Nonadiabatic Quantum Classical Molecular Dynamics in the Condensed Phase

Gardner,

Douglas-Gallardo,

Stark

et al. 2022

Preprint

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Accurate and efficient methods to simulate nonadiabatic and quantum nuclear effects in high-dimensional and dissipative systems are crucial for the prediction of chemical dynamics in condensed phase. To facilitate effective development, code sharing and uptake of newly developed dynamics methods, it is important that software implementations can be easily accessed and built upon. Using the Julia programming language, we have developed the NQCDynamics.jl package which provides a framework for established and emerging methods for performing semiclassical and mixed quantum-classical dynamics in condensed phase. The code provides several interfaces to existing atomistic simulation frameworks, electronic structure codes, and machine learning representations. In addition to the existing methods, the package provides infrastructure for developing and deploying new dynamics methods which we hope will benefit reproducibility and code sharing in the field of condensed phase quantum dynamics. Herein, we present our code design choices and the specific Julia programming features from which they benefit. We further demonstrate the capabilities of the package on two examples of chemical dynamics in condensed phase: the population dynamics of the spin-boson model as described by a wide variety of semi-classical and mixed quantum-classical nonadiabatic methods and the reactive scattering of H 2 on Ag(111) using the Molecular Dynamics with Electronic Friction method. Together, they exemplify the broad scope of the package to study effective model Hamiltonians and realistic atomistic systems. Methods FSSH NRPMD Ehrenfest MDEF Classical Langevin eCMM Simulation Parameters Atoms Elements Masses Models Analytic model ASE calculator ML model Keywords Simulation cell Temperature Method extras Simulation Method (atoms , model; kwargs¡ ) { } FIG. 1. The user inputs required to define the parameters for a simulation. This diagram along with Figs. 2 and 3 was created using the Excalidraw 65 online tool.

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Benchmarking Quasiclassical Mapping Hamiltonian Methods for Simulating Cavity-Modified Molecular Dynamics

Cited by 23 publications

References 94 publications

Commutator Matrix in Phase Space Mapping Models for Nonadiabatic Quantum Dynamics

Commutator Matrix in Phase Space Mapping Models for Nonadiabatic Quantum Dynamics

Forecasting nonadiabatic dynamics using hybrid convolutional neural network/long short-term memory network

NQCDynamics.jl: A Julia Package for Nonadiabatic Quantum Classical Molecular Dynamics in the Condensed Phase

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