2020
DOI: 10.1021/acs.jctc.9b01288
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Modeling Intermolecular and Intramolecular Modes of Liquid Water Using Multiple Heat Baths: Machine Learning Approach

Abstract: The vibrational motion of molecules in dissipative environments, such as solvation and protein molecules, is composed of contributions from both intermolecular and intramolecular modes. The existence of these collective modes introduces difficulty into quantum simulations of chemical and biological processes. In order to describe the complex molecular motion of the environment in a simple manner, we introduce a systembath model in which the intramolecular modes with anharmonic mode-mode couplings are described… Show more

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Cited by 20 publications
(14 citation statements)
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References 115 publications
(317 reference statements)
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“…Since we use the eigenstate representation of the system, it is also possible to improve the description of the reacting system by increasing the dimension of its configuration space, and by introducing a more complex and structured system-bath interaction, for example, with the help of machine learning approaches. 62,63 This provides a powerful tool to analyze the non-equilibrium reaction dynamics for rather complex PPCET reactions.…”
Section: Discussionmentioning
confidence: 99%
“…Since we use the eigenstate representation of the system, it is also possible to improve the description of the reacting system by increasing the dimension of its configuration space, and by introducing a more complex and structured system-bath interaction, for example, with the help of machine learning approaches. 62,63 This provides a powerful tool to analyze the non-equilibrium reaction dynamics for rather complex PPCET reactions.…”
Section: Discussionmentioning
confidence: 99%
“…172 Beyond explicit inclusion of environment atoms, dynamics can be propagated using model Hamiltonians for open quantum systems, 173 and ML has been applied to enable and accelerate the calculation of such dynamics. 126,[174][175][176] In a final approach, nonadiabatic events in the excited state are evaluated with QM methods on top of a ML ground-state dynamics (this approximation can be justified for very large assemblies, in which excited-state geometry deformation can be neglected). 42 In this case, time-derivative nonadiabatic couplings are still calculated with a QM method.…”
Section: [H2] Dynamics Of Large Systemsmentioning
confidence: 99%
“…For investigations of 2D spectroscopy, Seiji Ueno used machine learning techniques to create a SB model with ground and excited state PESs based on microscopic trajectories obtained from MD and quantum chemical calculations (127,136). The constructed model is then used to compute various 2D vibrational and electronic spectra using the HEOM approach.…”
Section: A 2d Spectroscopiesmentioning
confidence: 99%