Fusion Materials AI
Project Overview
University
Year
About
Fusion reactors require materials that can withstand intense radiation, high temperatures and repeated mechanical stress, yet testing and simulation can take months and depend on costly specialist facilities. Fusion Materials AI combines experimental data, physics-based modelling and machine learning within a digital model of reactor conditions. It predicts how radiation damage affects materials across different scales, enabling rapid virtual testing, alloy comparison and component lifetime assessment. Already used by nuclear research and industry partners, the platform could shorten analysis from months to hours, improve reactor safety and accelerate commercial fusion development.
Team
Saleem Lubbad
Saleem Lubbad is a UKRI Impact Acceleration Research Fellow at the University of Oxford and a Senior Engineering Analyst at the UK Atomic Energy Authority. He holds a PhD in the Science and Technology of Nuclear Fusion Energy from Oxford. His research interests span plasma–material interactions, virtual experiments on irradiated materials, and advanced tools for assessing critical reactor components. Beyond technical work, he is an Arabic-language poet, writer, and voice-over artist, interested in bridging science, literature, and cultural heritage. He has produced and presented science television programmes and curated museum exhibitions exploring cultural and scientific heritage in the Middle East.