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Simulating the ghost: quantum dynamics of the solvated electron

Jinggang Lan1*, Venkat Kapil2*, Piero Gasparotto3*, Michele Ceriotti2*, Marcella Iannuzzi1*, Vladimir Rybkin1*

1 Department of Chemistry, University of Zurich, Zurich, Switzerland

2 Laboratory of Computational Science and Modelling, Institute of Materials, Ecole Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland

3 Empa, Swiss Federal Laboratories for Materials Science & Technology, 8600 Dübendorf, Switzerland

* Corresponding authors emails: jinggang.lan@uzh.ch, vk380@cam.ac.uk, piero.gasparotto@empa.ch, michele.ceriotti@epfl.ch, marcella.iannuzzi@chem.uzh.ch, vladimir.rybkin@chem.uzh.ch
DOI10.24435/materialscloud:dz-a0 [version v1]

Publication date: Mar 18, 2021

How to cite this record

Jinggang Lan, Venkat Kapil, Piero Gasparotto, Michele Ceriotti, Marcella Iannuzzi, Vladimir Rybkin, Simulating the ghost: quantum dynamics of the solvated electron, Materials Cloud Archive 2021.46 (2021), doi: 10.24435/materialscloud:dz-a0.


The nature of the bulk hydrated electron has been a challenge for both experiment and theory due to its short lifetime and high reactivity, and the need for a high-level of electronic structure theory to achieve predictive accuracy. The lack of a classical atomistic structural formula makes it exceedingly difficult to model the solvated electron using conventional empirical force fields, which describe the system in terms of interactions between point particles associated with atomic nuclei. Here we overcome this problem using a machine-learning model, that is sufficiently flexible to describe the effect of the excess electron on the structure of the surrounding water, without including the electron in the model explicitly. The resulting potential is not only able to reproduce the stable cavity structure but also recovers the correct localization dynamics that follow the injection of an electron in neat water. The machine learning model achieves the accuracy of the state-of-the-art correlated wave function method it is trained on. It is sufficiently inexpensive to afford a full quantum statistical and dynamical description and allows us to achieve accurate determination of the structure, diffusion mechanisms, and vibrational spectroscopy of the solvated electron.

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Solvated Electron Quantum Dynamics Møller-Plesset Perturbation Theory Machine Learning SNSF CSCS MARVEL

Version history:

2021.46 (version v1) [This version] Mar 18, 2021 DOI10.24435/materialscloud:dz-a0