One of the major challenges of designing new substances is being able to predict the outcomes of chemical reactions. Predicting chemical phenomena could allow new medical therapies, as well as other technologies, to be developed much faster than is possible with traditional experimental chemistry approaches.
Paul Ayers is a leader in theoretical chemistry, and leads a research group that is developing computational and conceptual methods for understanding, predicting, interpreting and quantifying chemical phenomena. His research draws from expertise in chemistry, mathematics and computer programming. His wide-ranging contributions to the field have included purely mathematical concepts, unique algorithms and leading-edge computer programming.
This research focus is contributing to progress in developing new machine-learning methods for predicting the properties of molecules and materials, deciphering complex chemical reactions and designing new drugs.
Dr. Ayers, a 2013 recipient of an NSERC Steacie Fellowship, is now pursuing a new area of chemistry, strongly correlated systems, which includes molecular magnets and superconductors. He has developed computational techniques that would be applicable to this significant technological challenge. Future research will include converting such techniques into a computer program that puts these transformational ideas into practice.
His work in the field is significant as a fundamental theoretical interest, while also holding potential for application. For example, determining how a single molecule could behave like a tiny magnet could lead to advances in quantum computing, information storage, and refrigeration and air conditioning that is more environmentally friendly than current technologies.