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  Discovery of new materials for applications on glass using Deep Machine Learning and Data Analytics

   School of Electrical Engineering, Electronics and Computer Science

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  Prof J Y Goulermas, Prof M J Rosseinsky, Dr M Gaultois  Applications accepted all year round  Funded PhD Project (Students Worldwide)

About the Project

This opportunity will remain open until the position has been filled and so early applications are encouraged.

The functionalisation of flat glass is a multi-billion dollar industry spanning applications including displays technology, energy saving windows and energy generation through photovoltaics. To maintain the rate of progress in these and other emerging fields new materials are required with performance beyond that of known materials.

The interplay of many considerations including structure, bonding, and defect chemistry makes for a challenging opportunity to develop a new material that is stable and has interesting functional properties that can be applied as a thin film on glass. Machine learning and data analytics 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 emissivity, thermal conductivity, specific optical absorption. For example, 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, whereas large scale data visualisation and representation learning algorithms have been instrumental in capturing latent properties and hidden statistical characteristics in chemical data collections.

This PhD project will explore the possibility of applying existing methods and algorithms as well as developing novel ones to automate the processing of features and their combinations to predict various properties desirable for functional glass. The student will work closely with computer scientists, inorganic chemists, physicists, and material scientists to develop tools to predict new materials that may exhibit desirable properties. This may involve developing models to identify new chemistries or regions of the periodic table where these properties may occur, and/or identifying new ways to improve the properties in existing materials. The machine 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.

Qualifications: Applications are welcomed from students with a preferably 1st or 2.1 class BSc or equivalent in Mathematics, Computer Science, or Physics. Previous experience with developing machine learning models (or chemistry) 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). 

Informal enquiries should be addressed to Dr Troy Manning ([Email Address Removed]) 

Please apply by completing the online postgraduate research application form here at: and click on the 'Ready to apply? Apply online' button. Please ensure you quote the following project title and reference on your application: Discovery of high-temperature superconductors using Deep Learning (Reference LRC1903CS)

Chemistry (6) Materials Science (24) Mathematics (25)

Funding Notes

The funding for this position will be jointly between University of Liverpool and NSG. The award will pay full tuition fees and 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. Overseas students may be required to pay additional tuition fees.
The stipend will be paid in line with the standard UKRI rate and tuition fees covered at the UK rate. Non-UK applicants may have to contribute to the higher non-UK overseas fee.


Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials. Energy Environ. Sci., 10, 306-320 (2017)
Li4.3AlS3.3Cl0.7: A Sulfide–Chloride Lithium Ion Conductor with Highly Disordered Structure and Increased Conductivity. Chemistry of Materials, 33, 8733-8744 (2021);
Element selection for crystalline inorganic solid discovery guided by unsupervised machine learning of experimentally explored chemistry. Nature Communications 12, 5561 (2021);

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