Jinggang Lan^1*, Majed Chergui^2*, Alfredo Pasquarello^1*

1 Chaire de simulation à l’échelle atomique, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland

2 Lausanne Centre for Ultrafast Science (LACUS), ISIC, École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland

* Corresponding authors emails: jinggang.lan@epfl.ch, majed.chergui@epfl.ch, alfredo.pasquarello@epfl.ch

DOI10.24435/materialscloud:br-jf [version v1]

Publication date: Feb 05, 2024

How to cite this record

Jinggang Lan, Majed Chergui, Alfredo Pasquarello, Dynamics of the charge transfer to solvent process in aqueous iodide, Materials Cloud Archive 2024.21 (2024), https://doi.org/10.24435/materialscloud:br-jf

Description

Charge-transfer-to-solvent states in aqueous halides are ideal systems for studying the electron-transfer dynamics to the solvent involving a complex interplay between electronic excitation and solvent polarization. Despite extensive experimental investigations, a full picture of the charge-transfer-to-solvent dynamics has remained elusive. Here, we visualise the intricate interplay between the dynamics of the electron and the solvent polarization occurring in this process. Through the combined use of ab initio molecular dynamics and machine learning methods, we investigate the structure, dynamics and free energy as the excited electron evolves through the charge-transfer-to-solvent process, which we characterize as a sequence of states denoted charge-transfer-to-solvent, contact-pair, solvent-separated, and hydrated electron states, depending on the distance between the iodine and the excited electron. Our assignment of the charge-transfer-to-solvent states is supported by the good agreement between calculated and measured vertical binding energies. Our results reveal the charge transfer process in terms of the underlying atomic processes and mechanisms.

Materials Cloud sections using this data

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File name	Size	Description
data.zip MD5md5:9f585c4fda6ac6d473f1447df676d38e	73.3 MiB	It contains all needed for simulations: quantum trajectories, input files for classical and quantum dynamics, and machine learning potentials for molecular simulations.

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Keywords

machine learning molecular dynamics ab initio