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.
This research has direct implications for technologies where gas transport through organic materials is a limiting factor. For example, hydrogen crossover through polymer membranes is a key challenge in next-generation fuel cells, influencing both efficiency and safety. Similarly, gas diffusion and reactive transport in polymer components underpin degradation pathways in batteries, affecting lifetime and performance. By delivering accurate, multiscale models of gas–polymer interactions, this project will help AWE-NST (a UK stakeholder promoting fundamental science with practical impact) and the wider materials community design more durable polymer systems, optimise performance, and better predict long-term ageing behaviour.
About HetSys: Harnessing Data, Modelling and Simulation for Real‑World Impact
HetSys (Centre for Doctoral Training in Modelling of Heterogeneous Systems) at the University of Warwick is an innovative, interdisciplinary fully funded PhD programme that brings together science, engineering, and mathematics to tackle some of the most pressing challenges of our time.
If you’re excited by the idea of using advanced modelling and simulation to solve complex, real‑world problems, HetSys offers the perfect environment to push boundaries and make a difference.
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