PhD in Applied Mathematics

About the project: Machine Learning for Organic Materials: From Molecules to Mobility Supervisor: Prof. Gabriele Sosso, University of Warwick Accurately predicting how gases move through organic materials such as polymers underpins major challenges - from reducing hydrogen crossover in fuel cells to controlling gas transport that drives battery degradation. The key challenge is to build models that capture gas/polymer interactions and ageing with quantum-level accuracy at the larger scales of real materials. This project will train machine-learning models on high-quality quantum data, use them for molecular simulations, and connect the results to continuum models via reproducible multiscale approaches. The focus will be on gas/polymer systems relevant to AWE-NST, a UK stakeholder promoting fundamental science with practical impact.