• University of Stirling Featured PhD Programmes
  • Northumbria University Featured PhD Programmes
  • University of Southampton Featured PhD Programmes
  • University of East Anglia Featured PhD Programmes
  • University of Glasgow Featured PhD Programmes
  • Staffordshire University Featured PhD Programmes
  • University of Manchester Featured PhD Programmes
  • Queen’s University Belfast Featured PhD Programmes
University of Warwick Featured PhD Programmes
Helmholtz Zentrum München Featured PhD Programmes
ESPCI Paris Tech Featured PhD Programmes
University of Leeds Featured PhD Programmes
University of Sheffield Featured PhD Programmes

Discovery of high-temperature superconductors using advanced machine learning (Reference Bollegala LRC1119)


Project Description

High temperature superconductivity has great promise to transform society, though the underlying physics is complex and difficult to predict from first principles, and the space of possible materials is large and equally complex. 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 high-temperature superconductivity. 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 various properties related to superconductivity of materials.

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 high-temperature superconductivity. This may involve developing models to identify new chemistries or regions of the periodic table where superconducting states may occur, and/or identifying new ways to improve superconducting properties (such as the transition temperature) 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 temperature superconductivity.

The machine learning approaches applied will go far beyond the rather obsolete approaches deployed by physical computational science researchers thus far in the literature, instead using state-of-the-art approaches such as deep learning. 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 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 condensed matter physics is a benefit, though not required.

Please apply by completing the online postgraduate research application form here: https://www.liverpool.ac.uk/study/postgraduate-taught/applying/online/
Please ensure you quote the following reference on your application: Discovery of high-temperature superconductors using advanced machine learning (Reference Bollegala LRC1119

Funding Notes

The award is primarily available to students resident in the UK/EU and will pay full tuition fees and a maintenance grant for 3 years (£14,553 pa in 2017/18). Non-EU nationals are not eligible for this position and applications from non-EU candidates will not be considered unless you have your own funding.

Please note that this is a PhD Graduate Teaching Assistantships (GTA) and as such will have teaching commitments and contractual obligations to teaching associated with it.

References

Machine learning modelling of superconducting critical temperature. arXiv:1709.02727 [cond-mat.supr-con] https://arxiv.org/abs/1709.02727
Perspective: Web-based machine learning models for real-time screening of thermoelectric materials properties. APL Materials 4, 053213 (2016); http://aip.scitation.org/doi/10.1063/1.4952607

Email Now

Insert previous message below for editing? 
You haven’t included a message. Providing a specific message means universities will take your enquiry more seriously and helps them provide the information you need.
Why not add a message here
* required field
Send a copy to me for my own records.

Your enquiry has been emailed successfully




Cookie Policy    X