Accelerating energy landscape exploration through optimisation, approximation and parallelisation

Description

Many heuristic methods (random walks, probabilistic selection, genetic algorithms) for energy landscape exploration in Crystal Structure Prediction (CSP) [1] are very important material discovery tools [2]. However, the future of CSP lies in efficient search methods with an explainable outcome and a mathematical guarantee [1,3].

The proposed project will bring new algorithmic approaches and focus on:

• Improving performance guarantee of heuristic methods
• Design of new unbiased search methods
• Development and implementation of parallel algorithms to speed up the exploration.

The first direction is to automatise the selection of the permutation rules and their order of application in the form of structural and probabilistic strategies by evaluating the coverage of the configuration space of crystal structures. The second direction is to replace heuristic methods with combinatorial and geometric exploration strategies for the best composition and unit cell size. The final direction is to explore new ideas for the implementation of theoretical results within Quantum Computing and High-Performance Computing frameworks.

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.

[1] Nature 619, (2023) https://www.nature.com/articles/s41586-023-06071-y
[2] Digital Discovery (2023) https://pubs.rsc.org/en/content/articlelanding/2023/dd/d3dd00063j
[3] MFCS (2022) https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.MFCS.2022.8

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.