Solvated Electron: First Principles and Machine Learning Approaches

Explore a Lennard-Jones Centre discussion group seminar by Dr Jinggang Lan from EPFL on modeling solvated electrons using first principles and machine learning. Delve into the challenges of studying bulk hydrated electrons due to their short lifetime and high reactivity. Learn how a machine-learning model overcomes limitations of conventional empirical force fields by describing the excess electron's effect on surrounding water structure without explicit electron inclusion. Discover how this approach achieves state-of-the-art correlated wave function method accuracy while enabling full quantum statistical and dynamical description. Gain insights into the stable cavity structure, localization dynamics, vibrational spectroscopy, and temperature-dependent properties of solvated electrons. Understand the computational workflow and its applications to water under various thermodynamic conditions.

Intro
EPFL Solvated Electron
EPFL Challenges in modeling solvated electron
EPFL Aqueous Electron
EPFL Accurate modelling via machine learning method
EPFL Computational Workflow
EPFL Localization Dynamics of Solvated Electron
EPFL Vibrational Dynamics of Solvated Electron
EPFL Water at different thermodynamic conditions
EPFL Temperature-dependent Structure of Solvated Electron