SEGregation of residual eleMENTs at austenite grain boundaries in recycled steels (SEGMENT)

This PhD project spans across discipline boundaries, ranging from physics (atomistic modelling) to chemistry (thermodynamics, segregation), materials science (grain boundary structures) and manufacturing (steelmaking, recycling). The specific objectives are:

• Train a MLIP via active learning to capture impurity element segregation at grain boundaries in steel.
• Perform large-scale long-time atomistic simulations and employ sampling techniques to provide reliable predictions of grain boundaries.
• Develop a digital route to quantify and predict the segregation of residual elements to direct experimental investigations.

The combination of modern machine learning techniques with atomistic simulations will allow the computation of previously inaccessible larger geometries such as low-angle grain boundaries, which will also be linked to observations from the experimental partners.

The key outcomes of the project will be:

• A rigorously validated machine-learned interaction potential (MLIP) for impurity elements in steel.
• An in-depth understanding of the segregation tendency of copper and tin at the most common austenite/austenite grain boundaries in steel production.
• Links across several length scales feeding the data into existing continuum models for solidification and solid-state phase transformation, directing experimental investigations.
• A digital route or workflow for the prediction of alloy element segregation at grain boundaries in steels.

Further collaborations with UK-based steel producers such as Tata Steel, British Steel and Speciality Steel UK, which have a particular focus on steel grades produced via the electric arc furnace route. There will be opportunities to publish the work in academic journals, conference presentations and to connect with a network of PhD students working on steel through the IGNITE programme.

Technical skills: Machine learning and AI (active learning, sampling techniques, uncertainty quantification), atomistic simulation methods (Density Functional theory, molecular dynamics, MLIPs), high-performance computing and research software development (version control, testing, documentation).

Domain expertise: Steel manufacturing and recycling, alloy systems, grain boundaries.

Link to experiment: Close collaboration with experimental researchers in the Steels Group will allow you to develop a keen understanding of the capabilities and limitations of experimental techniques.

Professional and transferable skills: Python/C/Fortran programming, scientific communication, responsible research practice, collaboration with experiment and industry.

This skillset is in high demand within the UK engineering and particularly the UK metals sector to tackle challenges such as establishing a circular economy and improve sustainability of the heavy industries.

These skills also position you for careers in AI research, computational materials science, national laboratories, tech industry or academic research. The HetSys training provides a foundation for these skills through dedicated courses and cohort activities.

We require at least a II(i) honours degree at BSc or an integrated masters degree (e.g. MPhys, MChem, MSci, MEng etc.) in a physical sciences, mathematics or engineering discipline. We do not accept applications from existing PhD holders.

If you are an overseas candidate please check here that you hold the equivalent grades before applying.

For postgraduate study in HetSys, the term “overseas” or “international” student refers to anyone who does not qualify for UK home fee status. This includes applicants from the European Union (EU), European Economic Area (EEA), and Switzerland, unless they hold settled or pre-settled status under the UK’s EU Settlement Scheme.