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Simulating the ghost: quantum dynamics of the solvated electron

Chemistry

Simulating the ghost: quantum dynamics of the solvated electron

J. Lan, V. Kapil, et al.

This innovative research addresses the challenging nature of solvated electrons by using a machine-learning model to predict their impact on surrounding water structures. Conducted by Jinggang Lan, Venkat Kapil, Piero Gasparotto, Michele Ceriotti, Marcella Iannuzzi, and Vladimir V. Rybkin, the study successfully reproduces critical cavity structures and dynamics, paving the way for accurate insights into solvated electron behavior.... show more
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.
Publisher
Nature Communications
Published On
Mar 16, 2021
Authors
Jinggang Lan, Venkat Kapil, Piero Gasparotto, Michele Ceriotti, Marcella Iannuzzi, Vladimir V. Rybkin
Tags
solvated electron
machine learning
water structure
dynamics
vibrational spectroscopy
electronic structure
cavity structure
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