Anthony Poggioli

Anthony's research focuses on the implementation of neural network techniques for approximating reactive force fields from ab initio molecular dynamics (AIMD) simulations. Such techniques combine the versatility of ab initio calculations with the reduced computational cost of classical MD simulations. In particular, Anthony is focused on applying these techniques in the investigation of hydroxylation dynamics at solid-liquid interfaces under strong (nanometric or angstrometric) confinement and in the presence of fluid shear. This will for the first time allow for the investigation of the self-consistency relations between surface charge and fluid flow, as fluid flow both influences and is driven by the effective surface charge density at the solid-liquid interface. This work represents a next step in the molecular theory of electrokinetic processes and is highly relevant to, e.g., topics in membrane science such as blue energy generation and desalination.

Before joining Kavli ENSI, Anthony received a PhD in physics from the Ecole Normale Superieure in Paris, and before that he studied environmental engineering and hydrodynamics. Previously, his research has focused on topics related to transport in buoyant environmental flows and to nonlinear ionic transport in nanometric and angstrometric channels.