Computationally driving automated functional materials discovery for net zero technologies with machine reasoning and decision-making

Description

This project, suited to a student with a Computer Science or Mathematics background, will formally define the nature and consequences of the decisions that need to be made in the automated workflow and identify both the optimal combination of existing methods and tools to accelerate discovery and the gaps in capability that currently exist. The student will develop new methods and tools to address those gaps. Their project has the scope to span the entire process from initial suggestion of experimental targets through the autonomous assessment of experimental data produced by the automated workflow to the ultimate definition of experimental success in realising, rather than merely proposing, a new functional material. It offers the student the opportunity to both develop new methods and to participate in implementing them in a new workflow that will change how we find the materials that society will need in the future.

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.

The discovery of materials that will drive technologies for the net zero transition, such as batteries, solar absorbers, rare-earth-free magnets for wind power and myriad other unmet needs, is a scientific and societal grand challenge. Working in a cross-disciplinary team, the student will develop the decision-making and reasoning needed for an automated robot-based workflow that will accelerate this process. The project builds on very recent physical science (PS) progress in automated synthesis of extended inorganic solids [1] and computer science (CS) progress in the diffraction data analysis required to define discovery [2], and the guaranteed prediction of new materials [3].

Please apply by completing the online postgraduate research application form:

How to apply for a PhD programme

Please ensure you include the project title and reference number CCPR113 when applying.

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.