Explaining structure-property relations in the materials space

Description

This project aims to explain important materials properties from geometric invariants of crystal structures. Crystalline materials can be represented by invariants that distinguished different phases and polymorphs of all periodic materials in the Cambridge Structural Database and all known homometric structures with identical diffraction.

These structural invariants [1] are provably invertible to a full 3-dimensional structure for all generic crystals and implemented by our industry partner Cambridge Crystallographic Data Centre.

The next frontier is to understand the structure-property relationships by mapping important properties (energy, conductivity, adsorption capacity etc.) in the materials space as mountainous landscapes on a geographic-style map parametrised by the structural invariants.

New materials will be predicted in the digital space by optimising the existing structures [2] and exploring the blank regions that were missed by trial-and-error and random searches in the past.

The global need for researchers with capabilities in materials chemistry, digital intelligence and automation is intensifying because of the growing challenge posed by Net Zero and the need for high-performance materials across multiple sectors. The disruptive nature of recent advances in artificial intelligence (AI), robotics, and emerging quantum computing offers timely and exciting opportunities for PhD graduates with these skills to make a transformative impact on both R&D and society more broadly.

The University of Liverpool is therefore offering multiple studentships for students from backgrounds spanning the physical and computer sciences to start in October 2024. These students will develop core expertise in robotic, digital, chemical and physical thinking, which they will apply in their domain-specific research in materials design, discovery and processing. By working with each other and benefiting from a tailored training programme they will become both leaders and fully participating team players, aware of the best practices in inclusive and diverse R&D environments.

This training is based on our decade-long development of shared language and student supervision between the physical, engineering and computer sciences, and takes place in the Materials Innovation Factory (MIF), the largest industry-academia colocation in UK physical science. The training content has been co-developed with 35 industrial partners and is designed to generate flexible, employable, enterprising researchers who can communicate across domains. Applicants are advised to apply as soon as possible with applications considered when received and no later than 30/06/2024.

Please apply by completing the online postgraduate research application form:

How to apply for a PhD programme

We want all of our staff and Students to feel that Liverpool is an inclusive and welcoming environment that actively celebrates and encourages diversity. We are committed to working with students to make all reasonable project adaptations including supporting those with caring responsibilities, disabilities or other personal circumstances. For example, If you have a disability you may be entitled to a Disabled Students Allowance on top of your studentship to help cover the costs of any additional support that a person studying for a doctorate might need as a result.

Availability

Open to students worldwide

Funding information

Funded studentship

The EPSRC funded Studentship will cover full tuition fees of £4,786 per year and pay a maintenance grant for 4 years, starting at the UKRI minimum of £19,237 pa. for 2024-2025. The Studentship also comes with access to additional funding in the form of a research training support grant which is available to fund conference attendance, fieldwork, internships etc. EPSRC Studentships are available to any prospective student wishing to apply including international students. Up to 30% of our cohort can comprise of international students and they will not be charged the fee difference between UK and international rate.

Supervisors

References

[1] Resolving the data ambiguity for periodic crystals. NeurIPS 2022.

[2] Analogy Powered by Prediction and Structural Invariants. JACS 2022.