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Designing efficient search strategies for new materials discovery (Reference LRC1906CHEM)

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  • Full or part time
    Dr D Bollegala
    Prof M J Rosseinsky
    Dr M Gaultois
    Dr V Gusev
  • Application Deadline
    Applications accepted all year round

Project Description

This position will remain open until a suitable candidate has been found

There is an urgent need to develop new experimental strategies to discover functional materials. Because of the large numbers of possible components and synthetic routes to assemble them into materials, there is a vast experimental space of possibilities for materials synthesis which needs to be explored more efficiently. This project will use modern informatics techniques to develop new approaches to the discovery of materials.

Most functional materials are formed with less than 3 elements, and as we search for higher-performance materials with a particular function, we have a greater need to explore more complex systems. This presents incredible opportunities, but owing to the significant effort required to investigate even a single sample, the sheer number of possible sample compositions available in more complex phase space demands efficient search strategies in order to succeed.

This project will explore optimization methods and routines to develop optimal search strategies for new materials in high-dimensional search spaces, beginning with a single batch of experimental points, and expanding towards multi-batch searches incorporating information gained from previous batches using techniques such as X-ray diffraction.

Specifically, the student will work closely with computer scientists, inorganic chemists, physicists, and material scientists to develop software tools and active learning algorithms to choose experimental compositions in phase spaces with >5 elements, and to efficiently determine the compositions of new unknown materials. This will also involve exploring and benchmarking optimization strategies and developing visualization tools that accurately convey the optimization processes to users.

The student will acquire expertise in computer science and applied statistics methods for experimental design, and learn how to work as part of a multidisciplinary team to solve important scientific problems.

Qualifications: Applications are welcomed from students with a 2:1 or higher master’s degree or equivalent in Computer Science, Mathematics, 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.

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: Please ensure you quote: Designing efficient search strategies for new materials discovery (Reference LRC1906CHEM)

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 3.5years (£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.


Accelerated discovery of two crystal structure types in a complex inorganic phase field. Nature 546, 280-284 (2017);

Perspective: Composition–structure–property mapping in high-throughput experiments: Turning data into knowledge. APL Materials 4, 053211 (2016);

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