Discovery of materials for enhanced PV performance (Ref NSGPVPS2023)


   School of Electrical Engineering, Electronics and Computer Science

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

About the Project

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.

The award is 50% funded by NSG Group and 50% funded by the University of Liverpool through an EPSRC Doctoral Training Partnership award and will pay full tuition fees and a maintenance grant for 3.5 years. Applications from candidates meeting the eligibility requirements of the EPSRC are welcome – please refer to the EPSRC website (http://www.epsrc.ac.uk/skills/students/help/eligibility/). It provides full tuition fees and a stipend of approx. £17,668 (this is the rate from 01/10/2022) full time tax free per year for living costs. The stipend costs quoted are for students starting from 1st October 2022 and will rise slightly each year with inflation.

The funding for this studentship also comes with a budget for research and training expenses of £1000 per year, and for those that are eligible, a disabled students allowance to cover the costs of any additional support that is required.

Due to a change in UKRI policy, this is now available for Home, EU or international students to apply. However, please be aware there is a limit on the number of international students we can appoint to these studentships per year.

You will be encouraged to undertake some teaching duties for the department for which you will receive training and payment. You will have the option to work towards and apply for Associate Fellowship of the Higher Education Academy (via the Foundations in Learning & Teaching in Higher Education (FLTHE) programme https://www.liverpool.ac.uk/eddev/supporting-teaching/flthe/ or the University of Liverpool Teaching Recognition and Accreditation (ULTRA) Framework https://www.liverpool.ac.uk/eddev/ultra-cpd/).

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 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.

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 properties1–3. 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.


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

References

1. 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
2. Li4.3AlS3.3Cl0.7: A Sulfide–Chloride Lithium Ion Conductor with Highly Disordered Structure and Increased Conductivity. Chemistry of Materials, 33, 8733-8744 (2021); https://pubs.acs.org/doi/abs/10.1021/acs.chemmater.1c02751
3. Element selection for crystalline inorganic solid discovery guided by unsupervised machine learning of experimentally explored chemistry. Nature Communications 12, 5561 (2021); https://www.nature.com/articles/s41467-021-25343-7

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