‘Seeing’ new organic ferroelectrics (iCase joint with IBM Research UK)

Description: Ferroelectric materials have important applications, for example as non-volatile memory. However, this phenomenon is rare in organic materials, with the discovery process often led more though serendipity than design. Recent advances in crystal structure prediction and, more generally, high-throughput (virtual) screening techniques have paved the way for a more simulation and data-driven approach.

This student will develop state-of-the-art computation, pattern recognition algorithms, and machine learning to guide systematic experimental investigations in the Materials Innovation Factory into all-organic molecular room temperature ferroelectrics that are flexible, non-toxic, and solution processable. More generally, the project will build a new computational platform for the prediction of function from structure and available literature, through the use of information extraction and knowledge representation techniques.

Environment: This studentship will be based for at least 6 months at IBM Research UK within the Hartree Center in Daresbury, which was established to transform the competitiveness of UK industry by accelerating the adoption of High Performance Computing, Big Data and Cognitive technologies. Other Hartree focus areas include high accuracy formulation in consumer goods, manufacturing challenges and life sciences projects such as precision agriculture, anti-microbial surfaces and genomics. At the University, the studentship will be based in the Materials Innovation Factory (MIF), a new £68 M research facility, supervised by Prof. A. I. Cooper FRS, the MIF Academic Director. The studentship is funded by EPSRC but will also form a part of the Leverhulme Research Centre for Functional Materials Design, a new £10 M, 10-year activity funded by the Leverhulme Trust.

Qualifications: A 2:1 or higher degree or equivalent in Chemistry with a strong interest in data-science, or alternatively a strong interest in physical science but with Mathematics or Computer Science background. The candidate will be expected to have strong programming abilities (Python preferred), and an interest in the application of machine-learning techniques to complex