This project combines first-principles simulations based on density functional theory DFT with the development of automated and reusable computational workflows for muon studies in materials.
The goal of the project is to develop and apply advanced first-principles methodologies to determine muon stopping sites and muon-induced effects in materials, explicitly accounting for the quantum nature of the muon. Building on state-of-the-art DFT workflows for such simulations, you will extend existing approaches beyond classical treatments, incorporating quantum effects and modern data-driven techniques.
Starting with DFT-based calculations of muon stopping sites and migration pathways, including nudged elastic band NEB calculations, you will explore quantum treatments of the muon using approaches such as path-integral molecular dynamics PIMD and/or the stochastic self-consistent harmonic approximation SSCHA. You will further investigate the use of machine-learned interatomic potentials MLIPs to efficiently capture muon–material interactions and enable simulations at an affordable computational cost. Depending on interests and project evolution, you may also explore generative AI approaches to predict favorable muon stopping sites. We do not expect candidates to be experts in all these techniques at the start of the PhD; training and learning will be an integral part of the project.
A key component of the project is also the translation of these methods into robust, reusable, and user-friendly workflows, enabling their adoption by the broader µSR and materials-science communities. This includes contributing to and extending existing AiiDA-based workflows and graphical interfaces (e.g. AiiDAlab Quantum ESPRESSO applications) for automated muon simulations and analysis.
