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Development and Application of Deep Learning in materials chemistry

Project Description

In the quest for key advanced materials (e.g. cathodes and electrolytes for safer and higher capacity batteries) the interplay of many considerations including structure, bonding, and defect chemistry makes it challenging to develop a material that is stable and has the required performance e.g. ability 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 materials with enhanced properties such as ionic conductivity (batteries), transparent conduction (for displays and energy control in buildings), magnetism, multiferroicity, ferroelectricity (for low energy information storage) and catalytic performance (thermal, electro and photocatalysis for sustainable manufacturing and solar fuel generation).

Specifically, the student will work closely with computer scientists, inorganic chemists, physicists, and material scientists to develop tools to predict new high performance materials e.g., with high ionic conductivity (used as the example below), transparent conduction, magnetism or catalytic performance. 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.

These projects are appropriate for both computer science and numerate physical science undergraduate backgrounds, as they will be tailored to either the development of new approaches or the application of existing tools to the understanding and identification of outstanding new materials relevant to energy (batteries, superconductors), information storage (magnetism, ferroelectricity, multiferroicity) and catalysis applications. 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. Supervision is provided from Chemistry and Computer Science to appropriately support the discipline background of the student e.g., new method development for computer scientists with physical science understanding provided by Chemistry, or application of existing tools supervised by Computer Science to support physical scientists.

These projects form part of the Doctoral Training Centre for Next-Generation Materials Chemistry ( The University of Liverpool is offering 8 Ph.D positions starting October 2019 in this new Centre that will deliver a new cross-disciplinary approach to materials chemistry research. The Centre will train PhD graduates at the interface of physical science, AI, data science, and robotics to create the leaders in data-enabled science that UK industry and academia requires to deliver R&D 4.0. We seek applicants with a strong undergraduate background in chemistry, computer science, engineering, physics, mathematics or materials science for these posts.

Students in the Doctoral Training Centre for Next-Generation Materials Chemistry ( will be located in the newly opened Materials Innovation Factory (MIF -, which collocates academic and industrial researchers over 4 floors, with state-of-the-art automated research capabilities, including the £3M Formulation Engine. They will benefit from the cross-disciplinary training environment of the MIF, which contains staff from Physics and Computer Science as well as Chemistry, and the well-established community around the Leverhulme Research Centre in Functional Materials Design (, which is typified by a vibrant functioning engagement between physical science and computer science. Industrial partners include Unilever, Johnson Matthey and NSG Pilkington. Supervision is provided from both Chemistry and Computer Science, with the exact make-up of the supervisory team tailored to the students undergraduate background.

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. Successful candidates will have strong math and programming skills.

Name and email address to direct enquiries to:

Informal enquiries should be addressed to Troy Manning ().
Tel. No. for Enquiries: +44(0)151 794 3563

Please apply by completing the online postgraduate research application form here:
Please ensure you quote the following reference on your application: University of Liverpool Doctoral Training Centre in Next-Generation Materials Chemistry CDT03
Applications should be made as soon as possible.

Funding Notes

The award will pay full tuition fees and a maintenance grant for 3.5 years. The maintenance grant will be £15,007 pa for 2019-20. 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. One of the positions will have a requirement to work up to 88 hours/year in teaching-related activity in the Department of Chemistry and may be asked to teach up to 144 hours per year if required, with teaching above 88 hours being paid at the standard University demonstrator rate.


Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials. Energy Environ. Sci., 10, 306-320 (2017);

Machine learning modelling of superconducting critical temperature. arXiv:1709.02727 [cond-mat.supr-con]

Perspective: Web-based machine learning models for real-time screening of thermoelectric materials properties. APL Mater. 4, 053213 (2016);

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