Machine Learning to Unlock the Mysteries of Metallic Phase Transitions
This is a fully-funded 4-year PhD position based in the HetSys Centre for Doctoral Training at the University of Warwick.
Project outline
Join a PhD project that goes beyond state-of-the-art to explore the phase behaviour of potassium and unlock new understanding of alkali metals' unique physical properties. At high pressures and temperatures, these metals reveal complex phase transitions and exotic crystalline structures that remain poorly understood despite extensive experimental investigations.
This project combines cutting-edge sampling techniques with developing machine-learned potentials for accurate phase predictions, offering opportunities for method development with broad, long-term impact. Not only will you gain insights into fundamental atomistic properties of alkali metals, but you'll also contribute to pioneering computational tools that extend far beyond potassium.
Please note that due to the nature of AWE-NST's work, nationality restrictions apply to applications for this project.
Supervisors
Primary: Dr Livia Pártay (Chemistry)
Dr Albert Bartók (Engineering, Physics)
Project Partner: AWE NST
The overarching aim of this PhD project is to develop and apply robust computational approaches for predicting phase stability in metallic systems under extreme conditions. Specifically, the project will (1) elucidate the complex high-pressure and high-temperature phase behaviour of potassium, and (2) improve the thermodynamic fidelity and efficiency of machine-learned interatomic potentials trained on density functional theory (DFT) data.
To achieve these aims, the project will combine a novel unbiased sampling technique, nested sampling, and global optimisation tools to explore the potential energy landscape and identify stable and metastable crystal structures without prior assumptions. These data will form the foundation for constructing thermodynamically informed databases used to train advanced machine-learned potentials. Potassium provides an ideal test case, exhibiting a rich variety of crystalline phases and an anomalous melting curve that remains poorly understood. Insights gained here will guide the refinement of the methodology, enabling accurate and transferable modelling of metallic systems more broadly.
This project will deliver both scientific insights and methodological advances. Scientifically, it will provide a deeper understanding of the complex phase behaviour of potassium and other alkali metals, shedding light on the origins of their exotic crystalline structures and anomalous melting behaviour.
A key outcome will be a thermodynamically accurate machine learned interatomic potential model for potassium which will be made available for the wider community. Methodologically, a transferable workflow will be delivered to generate thermodynamically informed databases for constructing machine learning interatomic potential models.
The project will also assess the reliability and transferability of both the configuration-space sampling and the resulting models across wide temperature and pressure ranges, contributing to the development of next-generation interatomic potential models for materials simulation.
Throughout the project, you will gain in-depth experience with a wide range of computational techniques for evaluating phase stability, including advanced sampling methods, global optimisation, and crystal structure prediction approaches. You will also develop expertise in using state-of-the-art machine learning frameworks and efficient database-building strategies, together with methods for quantifying uncertainty in model predictions.
The project will provide a solid working knowledge of density functional theory (DFT) and practical experience in high-performance computing environments equipping you with a versatile and highly sought-after skill set applicable across computational materials science, chemistry, and data-driven research.
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.
If you are a European applicant without UK residency or immigration status that qualifies you for home fees, you will be classified as an overseas student.