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submitted on 23.06.2020 and posted on 26.06.2020by Jinggang Lan, Venkat Kapil, Piero Gasparotto, Michele Ceriotti, Marcella Iannuzzi, Vladimir V. Rybkin
The nature of bulk hydrated electron has been a challenge for both experiment and theory due to
its short life time 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 follows 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 a highly accurate determination of
the structure, diffusion mechanisms and vibrational spectroscopy of the solvated electron