Discovery of materials for enhanced PV performance (Ref NSGPVPS2023)

Description

An opportunity for a 3.5 year PhD position supported by NSG Group towards the computational discovery of new materials to enhance the performance of PV devices and forms part of a larger collaboration with NSG around the discovery of new materials for the glass industry.

This PhD project will explore the application of existing computer science methods and algorithms, as well as developing novel ones, to automate the processing of features and their combinations to predict various properties of materials. 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.

Ultimately the project aims to discover and develop new materials that can be applied as coatings on glass to enhance the performance of solar cell devices e.g. increasing the cooling from the top surface and will use machine learning and data analytics to propose new materials with the desired optical, electrical and thermal properties that can be synthesised by other researchers as part of the collaboration.

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.

As well as obtaining knowledge and experience in machine learning and data analytics, the student will develop skills in teamwork and scientific communication as computational and experimental researchers within the team work closely together.

Applications are welcomed from students with a preferably 1^st 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.

The inorganic materials chemistry group, led by Prof Rosseinsky at the University of Liverpool (https://www.liverpool.ac.uk/chemistry/research/rosseinsky-group/about/), focusses its research on the discovery of new solid inorganic compounds. Recently, the use of computational materials chemistry has accelerated this materials discovery process, leading to the synthesis of a range of novel metal oxides with a variety of functional properties^1–3. These successes have shown that the process of computer aided materials discovery relies on a close working relationship between computational and experimental researchers within the group, which is recognized in the EPSRC Programme Grant in Digital navigation of chemical space for function, and the decision to bring together theoretical and experimental researchers within the Materials Innovation Factory and the Leverhulme Centre for Functional Materials Design at the University of Liverpool. The successful candidate will participate in this relationship, using their experimental skills in close collaboration with the computational excellence present within the research group, to accelerate the discovery of new materials. The research will be performed in the Materials Innovation Factory with 2750 m² of top-quality research space on the top floor of the building.

Please apply by completing the online postgraduate research application form here: How to apply for a PhD - University of Liverpool

Please ensure you quote the following reference on your application: Discovery of materials for enhanced PV performance (Reference NSGPVPS2023) and apply for Computer Science.