Accurate simulations for Muon Spectroscopy
The ISIS facility at the Rutherford Appleton Laboratory (10miles south of Oxford) is capable of creating pulsed beams of muons for implantation into Materials. This enables Muon Spin Spectroscopy (μSR), a highly sensitive probe of the electronic and magnetic structure of materials.
The use of computer simulations for interpreting μSR experiments have been growing in recent years, as seen by the increasing number of μSR-related publications that include computational modelling. The modelling of the electronic structure is typically performed using solid-state Density Functional Theory (DFT) software, using commonly available exchange-correlation functionals. The current levels of precision are enough to, for instance, determine the muon stopping site when the muon does not diffuse or tunnel; or to decide on a scanning range for the magnetic field in an avoided level crossing experiment (ALC) in solids. However, there are an increasing number of studies where these levels of precision are not enough.
There are large parallels between the first-principles calculations of NMR and μSR parameters, and this project will take advantage of methods developed by Yates for NMR to improve the accuracy in the prediction of μSR parameters. The ability to accurately predict μSR spectra will both improve the planning of experiments and the interpretation of the experimental data for publication, making the facility instruments more productive.
The first objective is to explore the ability of new, and more accurate exchange-correlation for the prediction of muon hyperfine parameters (e.g. rSCAN). The second objective is to address the long-time scale molecular dynamics needed for an accurate description of thermal effects. For this we will make use of recent developments in machine learned inter-atomic potentials e.g. https://arxiv.org/abs/2412.15063.
This project will be in close collaboration with members of the STFC Scientific Computing Division, and with Staff Scientists at the ISIS facility.
For more information contact Prof. Jonathan Yates.
