Discovery of high-temperature superconductors using Deep Learning (Reference LRC1902CHEM)
High temperature superconductivity has great promise to transform society because it offers the transmission of electricity without loss of energy and the creation of large magnetic fields with applications including healthcare. 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. 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 deep learning approaches 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 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. Previous experience developing machine learning models is not a requirement, though successful candidates will have strong math and programming skills. An interest and/or coursework condensed matter physics is a benefit, though not required.
This position will remain open until a suitable candidate has been found.
Informal enquiries should be addressed to Prof Matthew Rosseinsky [Email Address Removed]
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 Deep Learning (Reference LRC1902CHEM)
The award is primarily available to students resident in the UK/EU and will pay full tuition fees and a maintenance grant for 3.5years (£14,777 pa in 2018/19). 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.
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