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Designing efficient search strategies for new materials (Ref: LRC1907CS)

Project Description

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

Reference LRC1907CS

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 develop and apply modern informatics techniques to develop new approaches to the discovery of materials in a close collaboration between computer scientists and physical scientists.

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 chemical problem can be stated as an optimal simplex exploration/sampling problem, and towards this goal, 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.

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 space 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.

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

Please apply by completing the online postgraduate research application form here: and quote reference: Designing efficient search strategies for new materials discovery (Reference LRC1907CS)

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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