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Dr D Bollegala
Prof M J Rosseinsky
No more applications being accepted
Funded PhD Project (European/UK Students Only)
About the Project
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 deep learning approaches applied will go far beyond the rather obsolete 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. 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 deep learning (Reference Bollegala LRC1119 )
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 rather obsolete 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. 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 deep 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.
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
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
Project supervisors
Dr D Bollegala's profile is coming soon
View other supervisors at University of LiverpoolCareer overview
Professor Matthew Rosseinsky studied Chemistry at the University of Oxford, where he received a BA in 1987 and a D. Phil in 1990. Following his studies, he joined A.T.&T. Bell Laboratories in Murray Hill, New Jersey, as a Postdoctoral Member of Technical Staff. In 1992, he returned to Oxford as a Lecturer in Inorganic Chemistry and a Student (Fellow) of Christ Church. In 1999, he moved to the University of Liverpool, where he holds the position of Professor of Inorganic Chemistry. Throughout his career, Professor Rosseinsky has received numerous awards, including the Harrison Memorial Prize in 1991, the Corday-Morgan Medal and Prize in 2000, and the Tilden Lectureship in 2006 from the Royal Society of Chemistry (RSC). In 2009, he was honoured with the inaugural De Gennes Prize from the RSC, recognising his lifetime achievements in materials chemistry. He has served as a Distinguished Lecturer in Inorganic Chemistry at Northwestern University in 2006, a Zernike Lecturer at Rijksuniversitat Groningen in 2009, and received the C.N.R. Rao Award from the Chemical Research Society of India in 2010. In 2017, he was the Muetterties Lecturer at the University of California, Berkeley, and the Lee Memorial Lecturer at the University of Chicago. In 2008, Professor Rosseinsky was elected to the Royal Society and was awarded the Hughes Medal in 2011 for his influential discoveries in the synthetic chemistry of solid-state electronic materials and novel microporous structures. He became a Royal Society Research Professor in 2013 and received the Davy Medal in 2017 for his advances in the design and discovery of functional materials, integrating new experimental and computational techniques. He was a member of the Science Minister’s Advanced Materials Leadership Council from 2014 to 2016 and served on the governing Council of the Engineering and Physical Sciences Research Council from 2015 to 2019. In 2019, he delivered the Flack Memorial Lectures for the Swiss Crystallographic Society and was awarded the Frankland Lectureship by Imperial College London. In 2020, he became an Honorary Fellow of the Chemical Research Society of India. In 2022, he presented the Davison Lectures at the Massachusetts Institute of Technology and received the Basolo Award from the Chicago Section of the American Chemical Society. In 2023, Professor Rosseinsky was awarded the Eni Energy Frontiers Award for the digital design and discovery of next-generation energy materials by the President of Italy.
Research interests
Professor Rosseinsky leads a research group focused on the design, discovery, synthesis, and characterisation of solid state materials. His work aims to enhance the fundamental understanding of the physical and chemical properties of new materials while improving their performance for applications in energy storage and generation, communications, and catalysis. He has made influential discoveries in the synthetic chemistry of solid state electronic materials and novel microporous structures.
Inorganic Chemistry
Solid State Materials
Materials Chemistry
Energy Storage
Energy Generation
Communications
Catalysis
Synthetic Chemistry
Microporous Structures
Physical Properties
Chemical Properties
Design and Discovery
Synthesis
Characterisation
Computational Techniques
Experimental Techniques
Advanced Materials
Functional Materials
Research Leadership
Academic Awards
Royal Society
Fellowship
Lectureships.
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