Location : Southampton, United Kingdom
Yearly income : £15,285 tax-free per annum for up to 3.5 years
Skyrmionics offers the potential for developing novel low-cost, energy-efficient information storage and processing technologies, or magneto-electronic sensors and devices. Fundamentally, skyrmions are excitations of matter, whose occurrence and collective properties remain only partially understood. This complicates the discovery of new materials capable of sustaining skyrmion phases that could be suitable for room-temperature device applications. The traditional “direct materials design” approach based on searching for novel compounds through extensive explorations of the chemical and physical parameter space remains challenging. This leads to the pursuit of the so-called “inverse design” to accelerate the discovery of new materials, closely integrating the machine learning tools, computational modelling, and experiments.
The aim of this PhD project is to devise inverse design strategies for identifying materials with target functionalities suitable for skyrmionics. We plan to adopt methodology combining machine learning, computational modelling based on a broad class of classical spin and micromagnetic models, and experimental material characterization data.
Our group within the Faculty of Engineering and Physical Sciences at the University of Southampton is renowned for data science and computational modelling, and has a long-term expertise in computational magnetism. The access to experimental data will be facilitated through close collaboration with our project partners within the UK’s National EPSRC Skyrmion project, which involves partners from Durham University, University of Southampton, University of Cambridge, University of Oxford, and Warwick University.