A PhD student and skyrmionior @University of Manchester, UK


About Yu Li

Hi, welcome to my personal website!

I have completed my BEng degree in Materials Physics, from the University of Science and Technology Beijing (USTB), China (2013 - 2017). During the last year of my undergraduate, I got a chance to spend a wonderful time on a research internship project in the Prof. Ming Liu’s group, in the Institute of Microelectronics, Chinese Academy of Sciences (IMECAS).

Then I kept to the path of research. I’m currently a PhD student in Skyrmionics team, part of the Nano Engineering and Spintronic Technologies (NEST) Group in the University of Manchester, UK (2017 - ). My project is supervised by Prof. Jim Miles and Dr. Christoforos Moutafis.

My recent research interests lie in the theoretical & computational studies of magnetic skyrmions (what’s magnetic skyrmion?[1]), and vary between many topics, e.g. skyrmion/antiskyrmion switching in chiral magnets[2], 3D dynamics and the energy landscapes of switching processes[3], roughness effect on the stability of skyrmionic textures in multilayer structures, ferromagnetic resonance, skyrmion-based devices for neuromorphic computing[4], etc.

I’m also having board collaborations with many people working on experiments, including people in our NEST group @Manchester, ETH Zurich, and Paul Scherrer Institute (PSI).

Contact me
Firstname.Secondname AT postgrad.manchester.ac.uk
IT 109, Kilburn Building, Oxford Road, Manchester, M13 9PL, UK

  1. ‘Magnetic skyrmion’ in wikipedia: https://en.wikipedia.org/wiki/Magnetic_skyrmion ↩︎

  2. L. Pierobon, C. Moutafis, Y. Li et al., Collective antiskyrmion-mediated phase transition and defect-induced melting in chiral magnetic films, Scientific Reports 8, 16675 (2018) ↩︎

  3. Y. Li, L. Pierobon, M. Charilaou et al., Tunable terahertz oscillation arising from Bloch-point dynamics in chiral magnets,Physical Review Research 2, 033006 (2020). ↩︎

  4. R. Chen, C. Li, Y. Li et al., Nanoscale room-temperature multilayer skyrmionic synapse for deep spiking neural networks, Physical Review Applied (accepted). ↩︎