Workshops

Electrochemical systems are dynamic ensembles of atoms and molecules, as well as particles that make up the electrode, where stochasticity plays a role. This workshop will focus on modeling techniques that explicitly consider stochasticity, such as ab initio-based molecular dynamics with explicit solvent molecules or stochastic potentiostating and microstructure-scale electrochemical dynamics models. The aim will be to understand which electrochemical quantities/processes require an explicit stochastic description and which can serve as benchmarks for the development of new models (such as implicit solvation (implicit/continuum) approaches) that faithfully include local fluctuations in space and time. Participants will explore the theoretical foundations of interaction kernels and their reduction via mean-field and hydrodynamic limits to effective implicit solvation methods. Crucially the goal is to incorporate fluctuations and randomness into the continuum limits and capture their impact on electrochemical processes. Practical applications of explicit methods in studying charge transfer reactions, ion transport, and electrode kinetics will be demonstrated. The incorporation of these effects into interaction kernels, in particular the requirements for multi-agent kernels, will be pursued. Discussions will revolve around best practices, numerical implementation, and the upscaling of explicit (stochastic) simulations into interaction kernel learning and cell-level models.

Topics include:

• Ab initio molecular simulations with explicitly included water and electric fields
• Advanced mathematical sampling strategies to overcome the limited time scales in ab initio simulations
• Microstructure-scale models for electrochemical dynamics that account for stochasticity
• Approaches in neighboring fields such as bioinformatics, molecular biology and drug design
• Stochastic learning of interaction kernels in potentiostat and thermostat design