Supervisors: Łukasz Figiel (WMG), L. Bartok-Partay (Chemistry)

In-service performance of functional materials for energy storage-related applications depends on an intimate interplay between various physical-chemical processes, e.g. strain-dependent ionic transport in polymer electrolytes. Classical electrochemical-mechanical (ECM) models, used to predict the optimum performance of those materials, are based on ad hoc assumptions, omitting the origins of the ECM coupling at the nanoscale.

Therefore, the ambition of this research project is to develop new foundations for a holistic computational modelling framework that rigorously captures the ECM coupling by bridging vastly different length scales via multiscale simulations of the material and machine learning. Particularly, the transport of charged species taking place in the presence of stress, electrostatic, and chemical potential gradients will be explored from nano to macro scale.

Case studies will focus on applying the developed computational modelling framework to study the electrochemical transport and mechanical damage to guide the design of new functional materials for future electrolytes and electrodes in solid-state batteries with enhanced electrochemical performance and damage resistance. Those case studies will be complemented by an uncertainty quantification (UQ) approach to determine probabilistic sensitivities of the model predictions to the model input.

In a broader view, this project will contribute to the development of an integrated predictive multiphysics modelling and simulation platform, for a multiscale design and analysis of advanced functional materials for applications in various sectors, from energy storage and renewable energy converting devices, to sensors and actuators in robotics.