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. Examples include ion motion and redox chemistry in batteries for transport and grid storage, solar absorbers for photovoltaic technologies, rare-earth-free magnets for wind power, catalysts for biomass conversion or water splitting for hydrogen generation, components in low-energy information technology and myriad other unmet needs.
This PhD project will tackle the synthesis in the laboratory of inorganic materials with unique structures that will expand our understanding of how atoms can be arranged in solids. The selection of experimental targets will be informed by artificial intelligence and computational assessment of candidates, working with a multidisciplinary team of researchers to maximise the rate of materials discovery. The resulting materials will be experimentally studied to assess their suitability in a range of applications, including targeting Li and Mg transport for advanced solid state battery materials. The student will thus both develop a strong materials synthesis, structural characterisation and measurement skillset, and the ability to work with colleagues across disciplines in a research team using state-of-the-art materials design methodology. The success of this approach is demonstrated in a range of papers (Science, 2024, 383, 739-745; J. Am. Chem. Soc., 2022, 144, 22178-22192; Science, 2021, 373, 1017-1022).
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 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). You will also make use of the first tools that use explainable symbolic AI to explore chemical space (Clymo, J., et al. Angew. Chem. Int. Ed., 2024) You will thus gain understanding of how the artificial intelligence and computational methods developed in the team accelerate materials discovery, and be able to contribute to the development of these models, which are designed to incorporate human expertise.
As well as obtaining knowledge and experience in materials synthesis, crystallography and measurement techniques, the student will develop skills in teamwork and scientific communication, as computational and experimental researchers within the team work closely together. There are extensive opportunities to use synchrotron X-ray and neutron scattering facilities.