This is an opportunity to undertake one of our new and exciting cross-disciplinary projects lying at the interface between computer science / mathematics and materials chemistry. The candidate does not need to have any knowledge in chemistry, but will need strong mathematical knowledge through a degree in maths, computer science, physics or engineering, as well as good programming skills.
We have a number of different problems to be investigated and the projects intend to develop both new models & theories and also practical applications. The broader research areas to be employed include mathematical modelling & optimisation, machine learning & data analytics, as well as algorithms and statistical analysis methods. Examples of such techniques include combinatorial and constrained optimisation, scale-space approaches, relaxation methods, neural networks, deep and reinforcement learning, statistical, unsupervised and supervised machine learning, signal/image processing, large-scale data visualisation, object sequencing, graph-based methods, topological data analysis, computational geometry, and any other methods that are potentially useful to solve the problems at hand.
The student will work closely with our very strong teams of computer scientists, mathematicians, inorganic chemists, physicists and material scientists to develop ways of predicting and analysing new materials. The supervisory team has a strong track record in the defining ingredients of the underlying work and will closely contribute to the originality of the research. Supervision is provided from both Computer Science and Chemistry departments to appropriately support the discipline background of the student. Publications in top-tier theoretical and also application-oriented venues will be expected. These 42 month PhD projects will tackle multidisciplinary problems co-defined by our industrial partners working with the University of Liverpool. Core training in robotics, automation, data science, etc., will form part of a unifying curriculum, together with leadership and entrepreneurship training, to underpin the individual research projects.
Background: Most current societal problems are limited by materials; for example, new electronics, faster computers, higher efficiency solar cells, higher performance catalysts and batteries that store more energy. All of these problems require new materials, which have to be discovered. This discovery process is difficult because each problem occurs in the combinatorial search space of all possible compositions within the periodic table, and one doesn’t know beforehand whether something exists. Further, the underlying interactions are complex, and many industries have decades of data studying a problem for several classes of materials, but the integration of these large historical datasets into a single model is challenging. Mathematical modelling, optimisation and machine learning methods have been shown to be promising in many complex problems and recent work has demonstrated that such methods may also be viable to predict new functional materials with desirable properties. Chemical applications may involve the discovery of better materials for automobile catalytic converters, industrial catalysis, transparent computer displays, new batteries and superconductors.
The award will pay full tuition fees and a maintenance grant for 3.5 years. The maintenance grant will be £15,007 pa for 2019-20. Non-EU applicants may have to contribute to the higher non-EU overseas fee.
The candidate should have at least a 2.1 BSc in Computer Science, Mathematics or related discipline, and also be competent in scientific programming (Matlab, R, Python, or C++).
Informal enquiries should be addressed Dr Vitaliy Kurlin – [email protected].
Tel. No. for Enquiries: +44 (0)151 794 8861
Please apply by completing the online postgraduate research application form.
Please ensure you quote the following reference on your application: University of Liverpool Doctoral Training Centre in Next-Generation Materials Chemistry CDT03
Applications should be made as soon as possible.
Students in the Doctoral Training Centre for Next-Generation Materials Chemistry will be located in the newly opened Materials Innovation Factory (MIF), which collocates academic and industrial researchers over 4 floors, with state-of-the-art automated research capabilities, including the £3M Formulation Engine. They will benefit from the cross-disciplinary training environment of the MIF, which contains staff from Physics and Computer Science as well as Chemistry, and the well-established community around the Leverhulme Research Centre in Functional Materials Design, which is typified by a vibrant functioning engagement between physical science and computer science. Industrial partners include Unilever, Johnson Matthey and NSG Pilkington. Supervision is provided from both Chemistry and Computer Science, with the exact make-up of the supervisory team tailored to the student’s undergraduate background.
The projects address the application of deep learning to inorganic materials chemistry. The inorganic materials chemistry group, led by Prof Rosseinsky at the University of Liverpool, 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. 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 Integration of Computation and Experiment for Accelerated Materials Discovery, 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 the development of computational models to guide experimental work and thus accelerate the discovery of new materials.