About the project:
Machine learning accelerated Inverse Design of Graphene Nanoribbons for Green Energy
Supervisor: Dr Sara Sangtarash, University of Warwick
Thermoelectric materials convert heat into electrical energy, crucial for sustainable power and waste heat recovery. Their efficiency is measured by the figure of merit, ZT. Achieving high ZT requires a delicate balance: high electrical conductance (G) and Seebeck coefficient (S) with low thermal conductance (k).
Graphene Nanoribbons (GNRs) are promising but currently, designing high-ZT GNRs is a slow, trial-and-error process, as the inverse problem is computationally intractable.
This project uses an AI-guided inverse design loop. A goal-directed AI proposes novel GNR architectures, which a fast machine learning model rapidly evaluates, accelerating the discovery of next-generation thermoelectrics.
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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