Modelling ultrafast dynamics at a conical intersection with regularized diabatic states: An approach based on multiplicative neural networks

Brey, Dominik; Koch, Werner; Martinazzo, Rocco; Burghardt, Irène

doi:10.1016/j.chemphys.2022.111542

Cited by 2 publications

(1 citation statement)

References 40 publications

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“…In this sense, it is rather challenging to simulate the multidimensional, ultrafast EET dynamics. The effective way to tackle this challenge is to use the multiconfiguration time-dependent Hartree (MCTDH) − method and its multilayer extension (ML-MCTDH). − These methods were widely used to simulate the excited-state electron or energy transfers of organic photovoltaic systems. − …”

Section: Introductionmentioning

confidence: 99%

Ultrafast Excited-State Energy Transfer in Phenylene Ethynylene Dendrimer: Quantum Dynamics with the Tensor Network Method

Liu,

Peng,

Bao

et al. 2024

J. Phys. Chem. A

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Photoinduced excited-state energy transfer (EET) processes play an important role in solar energy conversions. Owing to their excellent photoharvesting and exciton-transport properties, phenylene ethynylene (PE) dendrimers display great potential for improving the efficiency of solar cells. In this work, we investigated the intramolecular EET dynamics in a dendrimer composed of two linear PE units (2-ring and 3-ring) using a fully quantum description based on the tensor network method. We first constructed a diabatic model Hamiltonian based on the electronic structure calculations. Using this diabatic vibronic coupling model, we tried to obtain the main features of the EET dynamics in terms of the several diabatic models with different numbers of vibrational modes (from 4 modes to 129 modes) and to explore the corresponding vibronic coupling interactions. The results show that the EET in this PE dendrimer is ultrafast. Four modes of A' symmetry play dominant roles in the dynamics; the remaining 86 modes of A' symmetry can dampen the electronic coherence; and the modes of A″ symmetry do not exhibit significant influence on the EET process. Overall, the first-order intrastate vibronic coupling terms show the dominant role in the EET dynamics, while the second-order intrastate vibronic coupling terms cause damping of the electronic coherence and slow down the overall EET process. This work provides a microscopic understanding of the EET dynamics in PE dendrimers.

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Section: Introductionmentioning

confidence: 99%

Ultrafast Excited-State Energy Transfer in Phenylene Ethynylene Dendrimer: Quantum Dynamics with the Tensor Network Method

Liu,

Peng,

Bao

et al. 2024

J. Phys. Chem. A

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Recent advances in machine learning for electronic excited state molecular dynamics simulations

et al. 2022

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Machine learning has proven useful in countless different areas over the past years, including theoretical and computational chemistry, where various issues can be addressed by means of machine learning methods. Some of these involve electronic excited-state calculations, such as those performed in nonadiabatic molecular dynamics simulations. Here, we review the current literature highlighting recent developments and advances regarding the application of machine learning to computer simulations of molecular dynamics involving electronically excited states.

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Modelling ultrafast dynamics at a conical intersection with regularized diabatic states: An approach based on multiplicative neural networks

Cited by 2 publications

References 40 publications

Ultrafast Excited-State Energy Transfer in Phenylene Ethynylene Dendrimer: Quantum Dynamics with the Tensor Network Method

Ultrafast Excited-State Energy Transfer in Phenylene Ethynylene Dendrimer: Quantum Dynamics with the Tensor Network Method

Recent advances in machine learning for electronic excited state molecular dynamics simulations

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