Chemistry of Aqueous Oxide Interfaces from Machine Learning Molecular Dynamics

Explore the chemistry of aqueous oxide interfaces through machine learning molecular dynamics in this 43-minute conference talk by Annabella Selloni from Princeton University. Discover how deep neural network-based molecular dynamics simulations reveal the structure and properties of aqueous electrolyte-metal oxide interfaces, with particular focus on environmental, biological, and energy-relevant processes. Learn about the critical role these interfaces play in various applications and examine detailed case studies involving TiO2, a prototypical metal oxide widely used in photocatalysis and electrochemistry. Gain insights into the characterization of the Electrical Double Layer at TiO2-electrolyte interfaces under varying pH conditions and understand how external electric fields affect water dissociation at these interfaces. The presentation includes discussion of recent research findings published in Nature Communications and Proceedings of the National Academy of Sciences, providing cutting-edge perspectives on computational approaches to understanding complex interfacial chemistry phenomena.