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Discovery of solid electrolytes for new battery technology using Deep Learning (Reference LRC1904CHEM)

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  • Full or part time
    Dr D Bollegala
    Prof M J Rosseinsky
    Dr M Gaultois
    Dr V Gusev
  • Application Deadline
    Applications accepted all year round

Project Description

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

In the quest towards safer and higher capacity batteries, 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 conventional 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
.
Qualifications: 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.

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

Informal enquiries should be addressed to Prof Matthew Rosseinsky [Email Address Removed]

Please apply by completing the online postgraduate research application form here: https://www.liverpool.ac.uk/study/postgraduate-taught/applying/online/ please quote the reference when applying: Discovery of solid electrolytes for new battery technology using Deep Learning (Reference LRC1904CHEM)

Funding Notes

Depending on the successful applicant (EU or non-EU) this studentship would include a commitment to work up to 50 hours per academic year to help with teaching-related activities in modules currently taught in the Department of Chemistry, as assigned by the Head of Department or his representative. The award will pay full home/EU tuition fees and a maintenance grant (£15,007pa in 201919) for 3.5 years. Non-EU applicants may have to contribute to the higher non-EU overseas fee.

References

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

Machine learning modelling of superconducting critical temperature. arXiv:1709.02727 [cond-mat.supr-con] https://arxiv.org/abs/1709.02727

Perspective: Web-based machine learning models for real-time screening of thermoelectric materials properties. APL Mater. 4, 053213 (2016); http://dx.doi.org/10.1063/1.4952607



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