Header

UZH-Logo

Maintenance Infos

Simulating the ghost: quantum dynamics of the solvated electron


Lan, Jinggang; Kapil, Venkat; Gasparotto, Piero; Ceriotti, Michele; Iannuzzi, Marcella; Rybkin, Vladimir V (2021). Simulating the ghost: quantum dynamics of the solvated electron. Nature Communications, 12:766.

Abstract

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.

Abstract

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.

Statistics

Citations

Dimensions.ai Metrics
14 citations in Web of Science®
10 citations in Scopus®
Google Scholar™

Altmetrics

Downloads

20 downloads since deposited on 15 Nov 2021
17 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Department of Chemistry
Dewey Decimal Classification:540 Chemistry
Scopus Subject Areas:Physical Sciences > General Chemistry
Life Sciences > General Biochemistry, Genetics and Molecular Biology
Physical Sciences > General Physics and Astronomy
Uncontrolled Keywords:General Physics and Astronomy, General Biochemistry, Genetics and Molecular Biology, General Chemistry
Language:English
Date:1 December 2021
Deposited On:15 Nov 2021 09:28
Last Modified:27 Jan 2022 08:44
Publisher:Nature Publishing Group
ISSN:2041-1723
OA Status:Gold
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1038/s41467-021-20914-0
  • Content: Published Version
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)