Simulating the ghost: quantum dynamics of the solvated electron

• DOI: 10.1038/s41467-021-20914-0
• Publication Date: 2021-02-03T11:32:46Z
• Authors: Jinggang Lan; Venkat Kapil; Piero Gasparotto; Michele Ceriotti; Marcella Iannuzzi; Vladimir V. Rybkin
• Keywords: 10120 Department of Chemistry; 34 Chemical Sciences; Science; Q; General Physics and Astronomy; 1600 General Chemistry; Genetics and Molecular Biology; General Chemistry; 01 natural sciences; 3100 General Physics and Astronomy; Article; 0104 chemical sciences; 1300 General Biochemistry, Genetics and Molecular Biology; 540 Chemistry; General Biochemistry; 0103 physical sciences; 3406 Physical Chemistry; Machine Learning and Artificial Intelligence; Generic health relevance; 51 Physical Sciences

AbstractThe 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.