Workshops

Multi-scale approaches that link the atomistic scale to the electrode and then to the device scale are crucial for capturing the behavior of complex electrochemical systems. Coarse-graining and upscaling of models and simulations are essential in understanding and predicting observable behaviors of multiscale systems while accurately capturing phenomena at small temporal and spatial scales.  There are many challenges inherent in multiscale computation. Coarse-grained models must inform the environmental conditions for atomistic simulations, while properties of the materials originating from atomic-scale interactions must be accurately accounted for in higher-length-scale models.   Model validation requires predictions that can be compared to measurements.

One focus of the workshop is on the development of implicit solvation methods from interaction kernels trained upon stochastic differential equations modeling electrochemical systems. This effort seeks to incorporate temporal fluctuations crucial to electrochemical reactions into implicit solvent models. Other focuses include homogenization of electrode microstructures (including their stochasticity) to connect to the macroscale device behavior, as well as coarse-graining/up-scaling to connect atomistic properties to micro-scale/electrode-level phenomena. The ultimate goal is to obtain computationally tractable continuum-level models consistent with fundamental principles of thermodynamics. This includes derived effective boundary conditions that account for the atomic-scale phenomena such as interfacial segregation and structure. Mathematical challenges include efficient homogenization to continuum-level effective medium (e.g., implicit solvent models) and handling of stiff, nonlinear equations. Leading experts will share their insights into the theory, numerical techniques, and applications associated with interaction kernels, implicit models, and discuss their advantages and challenges.

Topics include:

• Mathematical and numerical upscaling techniques such as homogenization, volume averaging, sparse grid interpolation, neural networks.
• Deriving implicit solvation models from interaction kernels.
• Impact of multi-particle interaction kernels on complexity of energy landscape.
• Inverse problem: best approximation of implicit solvation models by interaction kernels.
• Deriving effective boundary conditions to account for interfacial phenomena.
• Mathematical and numerical methods to account for microstructures, both explicitly and implicitly.