Machine Learning for Aqueous Interfaces

Development of Machine Learning models to accurately represent interfacial regions of chemical systems, with particular interest in the modelling of the interface of clay minerals with their intrinsic interlayer water region. This research is aimed at the reproduction of ab initio accuracy of large systems with a much lesser computational time frame for the calculation, allowing for the investigation of phenomena usually limited by the time frame available.
The main research area of this project is the investigation of the uptake of environmentally damaging chemical species within the interlayer region of these clay systems.

Biography

I developed an interest into the environmental applications of chemistry from working with my father on the family farm, where he would talk to me about the delicate balancing act of soil and clay health for agricultural use and the use and make-up of artificial fertilisers. Having then completed my undergraduate studies in chemistry with a thesis in computational chemistry, the opportunity to study something I was extremely interested in using a prominent research method in both academia and industry presented itself.
Outside of research, I play hockey as a player-coach for Queen’s Mens 2nd XI, and I enjoy baking (badly) with my fiancé.

Research interest

• Machine Learning
• Agriculturally relevant chemical systems
• Clay minerals