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Computational identification of new functional materials combining machine learning and structure prediction


Project Description

New higher performance functional materials are needed to improve the energy efficiency and sustainability of society, for example in battery technology, solar harvesting of energy, and catalysis for sustainable manufacturing. These materials often have complex elemental compositions and crystal structures, which it makes it extremely difficult to find them efficiently.

This project will combine the application of machine learning methods that identify the most likely parts of chemical space where new materials will be located, with crystal structure prediction methods that allow the compositions and structures of the materials to be predicted. Combining these approaches offers a route to accelerating the discovery of functional materials. We have developed new approaches to both of these problems1-3.

The student will learn how to apply and develop machine learning and structure prediction tools to identify new candidate materials which will be synthesised by experimental collaborators within our research team. The student will work closely with computer scientists, inorganic chemists, physicists, and material scientists to develop and apply software tools

Qualifications: Applications are welcomed from students with a 2:1 or higher master’s degree or equivalent in Chemistry, Physics, Computer Science, Mathematics or Materials Science, particularly those with some of the skills directly relevant to the project outlined above. Successful candidates will have strong math and programming skills.

To apply please visit, https://www.liverpool.ac.uk/study/postgraduate-research/how-to-apply/ and click the ‘Ready to apply? Apply online’ button

Funding Notes

EPSRC eligibility
Please refer to the EPSRC website View Website

The award will pay full tuition fees and a maintenance grant for 3.5 years. The maintenance grant is £15,009 pa for 2019-20, with the possibility of an increase for 2020/21.

GTA eligibility (EU or non-EU students only)
Depending on the successful applicant this studentship would include a commitment to work up to 77 hours per academic year to help with teaching-related activities. The award will pay full home/EU tuition fees and a maintenance grant for 3.5 years. Non-EU applicants may have to contribute to the higher non-EU overseas fee.

References

1. Dyer, M. S.; Collins, C.; Hodgeman, D.; Chater, P. A.; Demont, A.; Romani, S.; Sayers, R.; Thomas, M. F.; Claridge, J. B.; Darling, G. R.; Rosseinsky, M. J., Computationally Assisted Identification of Functional Inorganic Materials. Science 2013, 340 (6134), 847-852.
2. Collins, C.; Darling, G. R.; Rosseinsky, M. J., The Flexible Unit Structure Engine (FUSE) for probe structure-based composition prediction. Faraday Discuss. 2018, 211 (0), 117-131.
3. Collins, C.; Dyer, M. S.; Pitcher, M. J.; Whitehead, G. F. S.; Zanella, M.; Mandal, P.; Claridge, J. B.; Darling, G. R.; Rosseinsky, M. J., Accelerated discovery of two crystal structure types in a complex inorganic phase field. Nature 2017, 546 (7657), 280-284

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