Machine learning accelerated electronic transport calculations for complex materials
This is a fully-funded 4-year PhD position based in the HetSys Centre for Doctoral Training at the University of Warwick.
Project outline
Advancements in materials synthesis have allowed the realization of many novel materials and their alloys, which are gradually finding their ways into numerous applications including energy, sustainability, medicine, novel computation, etc.
A major direction of interest is their electronic properties. However, the accurate assessment and prediction of electronic transport is a highly challenging task.
The project uses Machine Learning (ML), in combination with DFT and state-of-the-art Boltzmann transport methods, to predict, accelerate, and scale the computation of electronic properties of complex materials and their alloys.
The richness of experimental data from literature and project partners will aid towards model validation.
Supervisors
Primary: Prof. Neophytos Neophytou (Engineering)
Prof. James Kermode (Engineering)
Prof. Reinhard Maurer (Chemistry)
Project Partner: University of St Andrews
The project uses Machine Learning (ML) to predict, accelerate, and scale the computation of electronic properties of complex materials and alloys.
In a typical calculation we use Boltzmann transport simulators, which take as input the electronic structure (extracted from ab initio or from effective Hamiltonians) and the scattering rates that the electrons experience as they propagate in the material. The latter is the most computationally expensive part, which requires the evaluation of the interaction of electrons and phonons.
The focus of this project is to accelerate this through ML, such that it can be scaled for a wide range of new materials and their alloys. The project will accelerate the Boltzmann transport simulations as well, closing the complete path of feasible and scalable computations from crystal structure all the way to electronic transport properties based on ML.
The project partners with experimental collaborators to test model predictions for high mobility materials.
- Development of a high-throughput computational framework that evaluates scattering rates for electronic transport in novel materials and their alloys.
- The acceleration of the framework developed (above) using ML techniques.
- Scalable studies for the electronic properties of materials and prediction of possible high mobility materials that hopefully are tested experimentally.
- Development of ML methods for semiclassical transport that can bypass Boltzmann transport simulations.
Robust software engineering: Develops electronic transport software.
Uncertainty quantification: Highly disordered material systems by nature involve uncertainty.
SciML: Develops physics-informed ML models to accelerate the prediction of electron-phonon scattering rates and electronic transport.
International exposure: Visits to the co-supervisors's lab in the University of Vienna, Austria.
Experimental collaboration: Collaboration with experimental partners (St Andrews, TU Vienna).
Scientific presentation: Advance the skills of presenting in formal scientific conferences.
Data science: Transferable skills in data science, Python/C++ programming, statistical analysis, problem-solving in complex systems.
These skills 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.
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.