Transparent conductors are essential for the sustainable future of advanced societies as they are key to the reduction of the energy demand in buildings, and also as transparent electrodes in solar energy conversion. New classes of material are needed to meet intensifying future demands. There are considerable barriers to discovering the required materials. The underlying physics is complex and difficult to predict from first principles, and the space of possible materials is large and equally complex. In particular, the materials we seek combine two properties that are antagonistic to each other – optical transparency and electrical conduction (metals normally reflect, rather than transmit, light). 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. For example, neural networks and 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 such methodologies as well as other machine learning and mathematical/statistical data analytics algorithms to model, predict and analyse various properties related to transparent conductors.
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 transparent conduction. This may involve developing models to identify new chemistries or regions of the periodic table where transparent conductors may occur, and/or identifying new ways to improve the required properties (such as lowering optical absorption while increasing conductivity) in existing materials. As a part of this goal, the student will work with both computed and experimental literature/database information to build models and descriptors to identify shared features in known materials that correlate strongly with the presence of transparent electron conduction.
The machine learning and mathematical algorithms 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 team’s understanding of materials chemistry and physics together with the PhD candidate’s background and developing expertise in machine learning and artificial intelligence approaches.
The PhD position is part-funded by NSG Pilkington who are a leading manufacturer of glass for sustainable buildings that requires next-generation transparent conductors. The student will join a team of researchers working with NSG including synthetic chemists making the materials, materials scientists depositing these materials as films and computational chemists calculating stability and properties. This is thus a unique opportunity to develop and apply new deep learning methods to a technology problem as part of an integrated team working with a leading UK manufacturer.
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. Successful candidates will have strong math and programming skills. An interest and/or coursework in condensed matter physics is a benefit, though not required.
This position will remain open until a suitable candidate has been found.
Please apply by completing the online postgraduate research application form here: https://www.liverpool.ac.uk/study/postgraduate-research/how-to-apply/
Please ensure you quote the following reference on your application: Discovery of transparent conductors for sustainable buildings using Deep Learning (Reference NSGML2020CS)
Informal enquiries should be addressed to Prof Yannis Goulermas [email protected]
Supervisors: Prof. Yannis Goulermas, Prof Matthew Rosseinsky, Dr Mike Gaultois, Dr Vladimir Gusev, Dr Dmytro Antypov