Next Generation Catalyst Development through Artificial Intelligence

Project Description

The development of impactful new catalysts is often hampered by lack of understanding how key structural motifs provide access to desirable properties and how to rationally synthesis materials to maximize these structures effectiveness toward reactivity. The result has arguably caused a split in methodologies toward materials development, either adhere to trial-and-error approaches that often ignore the underlying fundamental science or dig deep into developing structure/function relationships at the cost of possible speed in discovery. This project aims to speed-up the development of new enhanced catalysts by fusing synchrotron characterization with artificial intelligence to quickly and unbiasedly enable new synthetic routes.

Ideal Candidate

The ideal candidate will need to have some experience in one of the following areas: 1) materials characterization using synchrotron techniques; 2) computational materials chemistry; 3) artificial intelligence, 4) materials synthesis and catalysis characterization. While having experience is multiple areas is a desirable, it is not required as a prospective candidate will be working in a highly interdisciplinary team with supervisory experience in each of these areas. The candidate should also be able to work in a team environment and be willing to be the conduit between experimental and computational efforts.

Supervisory Team: Dr Nicholas Bedford (UNSW), Scientia Prof. Rose Amal (UNSW) and Dr Amanda Barnard (CSIRO Data 61)

For more information go to UNSW Scientia PhD Scholarships page