Recommended by

Indexed by

Temperature dependent properties of the aqueous electron

Jinggang Lan1*, Vladimir Rybkin2*, Alfredo Pasquarello1*

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

2 HQS Quantum Simulations GmbH, Haid-und-Neu-Straße 7, D-76131 Karlsruhe, Germany

* Corresponding authors emails: jinggang.lan@epfl.ch, vladimir.rybkin@quantumsimulations.de, alfredo.pasquarello@epfl.ch
DOI10.24435/materialscloud:cv-0v [version v1]

Publication date: Jul 20, 2022

How to cite this record

Jinggang Lan, Vladimir Rybkin, Alfredo Pasquarello, Temperature dependent properties of the aqueous electron, Materials Cloud Archive 2022.97 (2022), doi: 10.24435/materialscloud:cv-0v.


The temperature-dependent properties of the aqueous electron have been extensively studied using mixed quantum-classical simulations in a wide range of thermodynamic conditions based on one-electron pseudopotentials. While the cavity model appears to explain most of the physical properties of the aqueous electron, only a non-cavity model has so far been successful in accounting for the temperature dependence of the absorption spectrum. Here, we present an accurate and efficient description of the aqueous electron under various thermodynamic conditions by combining hybrid functional-based molecular dynamics, machine learning techniques, and multiple time-step methods. Our advanced simulations accurately describe the temperature dependence of the absorption maximum in the presence of cavity formation. Specifically, our work reveals that the red shift of the absorption maximum results from an increasing gyration radius with temperature, rather than from global density variations as previously suggested.

Materials Cloud sections using this data

No Explore or Discover sections associated with this archive record.


File name Size Description
416.4 MiB Data files including the MLPs, the data sets, the input files, and the molecular dynamics trajectories.


Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.


Aqueous Electron ab initio molecular dynamics machine learning hybrid functional MARVEL

Version history:

2022.97 (version v1) [This version] Jul 20, 2022 DOI10.24435/materialscloud:cv-0v