Automated solid state synthesis robotic workflow

Description

The experimental discovery of new inorganic materials shows us how crystal structure and chemical composition control physical and chemical properties. It is therefore critical for our ability to design functional materials with the properties we will need for the next zero transition. The use of robotic methods can greatly accelerate the discovery of new materials and when combined with optimisation techniques can be run autonomously to identify new materials with properties of interest.

This project will develop and exemplify a robotic workflow to perform solid state chemistry reactions, consisting of an automated weighing and mixing stage, coupled with a high temperature furnace to perform the reactions. Automated powder diffraction will be integrated to identify new materials within the phase fields being explored. The student will work closely with colleagues in the group of Prof Andy Cooper who have pioneered the use of autonomous robotic chemical synthesis for functional materials discovery. The project builds on a high throughput synthetic workflows developed in the group using slurry (Chem. Sci. 15, 2640, 2024.) and solution based precursors.

The project is based in the Materials Innovation Factory (https://www.liverpool.ac.uk/materials-innovation-factory/) at the University of Liverpool, a state-of-the-art facility for the digital and automated design and discovery of materials. The project will make use of tools developed in the multi-disciplinary EPSRC Programme Grant: “Digital Navigation of Chemical Space for Function” and the Leverhulme Research Centre for Functional Materials Design, that seek to develop a new approach to materials design and discovery, exploiting machine learning and symbolic artificial intelligence, demonstrated by the realisation of new functional inorganic materials. Examples include the first tools to guarantee the correct prediction of a crystal structure (Nature 68, 619, 2023), and to learn the entirety of known crystalline inorganic materials and guide discovery (Nature Communications 12, 5561, 2021).

The project will allow the student to develop knowledge and experience in automation and programming as well as solid state synthesis, crystallography and measurement techniques. The student will also develop skills in teamwork and scientific communication, as computational and experimental researchers within the team work closely together to progress the projects.

Applications are welcomed from students with a 2:1 or higher master’s degree or equivalent in Chemistry, Physics, Materials Science, Robotics, Computer Science or Engineering particularly those with some of the skills directly relevant to the project outlined above. Experience in programming would be an advantage.

This position will remain open until a suitable candidate has been found. 

Applications from candidates meeting the eligibility requirements of the EPSRC are welcome – please refer to the EPSRC website http://www.epsrc.ac.uk/skills/students/help/eligibility/.

The studentship is open to Home, EU and international students, however, please be aware there is a limit on the number of international students we can appoint per year. Further the studentship does not cover international fees

Please ensure you include the project title and reference number CCPR134 when applying.

Availability

Open to UK applicants

Funding information

Funded studentship

Funding will cover full home (UK national) tuition fees and a stipend set at the UKRI rate for a period of 3.5 years. The stipend amount for students starting in the 2024/2025 academic year is £19,237 and will rise slightly each year with inflation.

The funding for this studentship also comes with a budget for research and training expenses of £1000 per year, and for those that are eligible, a disabled students allowance to cover the costs of any additional support that is required.

Supervisors

References

[1] C. J. Hampson et al., A high throughput synthetic workflow for solid state synthesis of oxides, Chem. Sci. 15, 2640-2647, (2024).[2] V. V. Gusev et al., Optimality guarantees for crystal structure prediction, Nature 619, 68-72, (2023).[3] A. Vasylenko et al., Element selection for crystalline inorganic solid discovery guided by unsupervised machine learning of experimentally explored chemistry, Nature Commun. 12, 5561, (2021).