Solvated electron from first principles and machine learning скачать в хорошем качестве

Solvated electron from first principles and machine learning

Lennard-Jones Centre discussion group seminar by Dr Jinggang Lan from the École Polytechnique Fédérale de Lausanne (EPFL). 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. This talk explains how to 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 enables the achievement of accurate determination of the structure, diffusion mechanisms, vibrational spectroscopy and temperature-dependent properties of the solvated electron. The seminar was held on 15th May 2023. 🖥️ Check out our websites: https://linktr.ee/cumaterials