'Seeing' new organic ferroelectrics

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.