New materials are the foundation of the development of key technologies such as advanced batteries for electric vehicles, solar cells, catalysts for clean energy, and more. However, discovering new materials is currently a time consuming and expensive process: at present we do not have the experimental tools with the scale and speed to efficiently explore the vast chemical space available, and the current process that can take up to decades. The recent advances in robotics and artificial intelligence could dramatically speed up development of new materials and change the image of the chemical sciences. One of the challenges in the reform is on handling and evaluating the materials properties using robotic solutions to simulate the delicate manual operations of human chemists, for instance, shaking and transferring a pipette. This project will develop automated robotic platforms that can manipulate and handle the materials with rapid synthesis and characterization of materials. The developed systems would efficiently explore the discovery of new materials and accelerate the pace of discovery. The student will design and execute experiments on state-of-the-art robotic synthesis platforms, develop the required measurement approaches to extract and analyse data from the arrays of materials prepared using the robots, and investigate representations of the available knowledge in order to support automated reasoning about the best set of subsequent experiments.
The project will take place in the Materials Innovation Factory that forms part of the Leverhulme Research Centre for Functional Materials Design (LRC), and the state-of-the-art robotics lab smARTLab at the Department of Computer Science, University of Liverpool. The LRC and the smARTLab will provide support in both automation development and the development of appropriate experimental design approaches, exploiting the close interactions between physical and computer science. The project will be part of the LRC’s Intelligent Automation theme.
Qualifications: Applications are welcomed from students with a 2:1 or higher masters degree or equivalent in Computer Science or Materials Science, with good programming skills, particularly those with some of the skills directly relevant to the project outlined above.
This post will remain open until a suitable candidate is found.
Informal enquiries should be addressed to Dr Shan Luo (email@example.com).
Please apply by completing the online postgraduate research application form here.
Open to EU/UK applicants
This studentship include a commitment to work up to 72 hours per academic year to help with teaching-related activities in modules currently taught in the Department of Computer Science, as assigned by the Head of Department or his representative. The award will pay full home/EU tuition fees and a maintenance grant (£15,007pa in 2019/20) for 3.5 years. Non-EU applicants may have to contribute to the higher non-EU overseas fee.
 Tabor, D.P., Roch, L.M., Saikin, S.K., Kreisbeck, C., Sheberla, D., Montoya, J.H., Dwaraknath, S., Aykol, M., Ortiz, C., Tribukait, H. and Amador-Bedolla, C., 2018. Accelerating the discovery of materials for clean energy in the era of smart automation. Nat. Rev. Mater., 3, pp.5-20.
 LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-44.