This opportunity will remain open until filled and so early applications are encouraged.
In the quest towards safer and higher capacity batteries to enable electrification and a net zero society, the development of an all-solid-state battery is a top priority, and is currently limited by the lack of a high-performance material to serve as a solid state electrolyte. The interplay of many considerations including structure, bonding, and defect chemistry makes for a challenging opportunity to develop a material that is stable and is able to rapidly conduct ions in the solid state. Machine learning methods have been successfully applied to many complex problems, and recent work has demonstrated such methods may also be viable to predict new functional materials with desirable properties, such as ionic conductivity. In particular, deep learning methods have attracted attention for their ability to consider complex combinations of multiple attributes/features in a nonlinear fashion to predict structured outputs. This PhD project will explore the possibility of using deep convolutional neural networks to extract feature combinations and predict various properties related to the ionic conductivity of materials.
Specifically, the student will work closely with computer scientists, inorganic chemists, physicists, and material scientists to develop tools to predict new materials that may exhibit high ionic conductivity. This may involve developing models to identify new chemistries or regions of the periodic table where high ionic conductivity may occur, and/or identifying new ways to improve ionic conductivity in existing materials. As a part of this goal, the student will build models and descriptors to identify shared features in known materials that correlate strongly with the presence of high ionic conductivity.
The deep learning approaches applied will go far beyond the rather obsolete approaches deployed by physical computational science researchers thus far in the literature. This will be combined with the development of appropriate descriptors that use the teams understanding of materials chemistry and physics.
The award will pay full tuition fees and a maintenance grant for 3.5 years. The maintenance grant will be at the UKRI rate, currently £15,609.00 per annum for 2021-22, subject to possible increase.
The award will pay full home tuition fees and a maintenance grant for 3.5 years. Non-UK applicants may have to contribute to the higher non-UK overseas fee.
The stipend will be paid in line with the standard UKRI rate and tuition fees covered at the UK rate.
Applications are welcomed from students with a 2:1 or higher master’s degree or equivalent in Computer Science, Chemistry, Physics, or Materials Science, particularly those with some of the skills directly relevant to the project outlined above. Previous experience developing machine learning models is not a requirement, though successful candidates will have strong math and programming skills.
Applications from candidates meeting the eligibility requirements of the EPSRC are welcome – please refer to EPSRC website.
Students are encouraged to undertake some teaching duties for the Department, up to a maximum of 6 hours per week in term time, for which they will receive training and be paid at the regular hourly rate (currently £15.99 per hour).
For any enquiries please contact Dr Michael Gaultois on: [email protected]
To apply, please visit: https://www.liverpool.ac.uk/study/postgraduate-research/how-to-apply/ and click on the ‘Ready to apply? Apply online’ button. Please ensure you quote the following reference on your application: Reference CCPR035 Discovery of solid state electrolytes using Deep Learning.
Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials. Energy Environ. Sci., 10, 306-320 (2017)http://dx.doi.org/10.1039/C6EE02697D
Li4.3AlS3.3Cl0.7: A Sulfide–Chloride Lithium Ion Conductor with Highly Disordered Structure and Increased Conductivity. Chemistry of Materials, 33, 8733-8744 (2021); https://pubs.acs.org/doi/abs/10.1021/acs.chemmater.1c02751
Element selection for crystalline inorganic solid discovery guided by unsupervised machine learning of experimentally explored chemistry. Nature Communications 12, 5561 (2021); https://www.nature.com/articles/s41467-021-25343-7