Office: room 348, Kathleen Lonsdale Building, UCL
Tel: +44 (0) 20 76797909
Ext: 379 09
My research will focus on the development of highly accurate water potentials at interfaces and surfaces using machine learning approaches. Current research is often hampered by a tradeoff between obtaining an accurate description of a system of interest while simultaneously minimising computational costs. There is no fundamental reason why a molecular dynamics potential could not perfectly reproduce the dynamics and structures obtained with expensive ab-initio methods; machine learning techniques represent a viable method for finding this potential.
University of Warwick – 2015-2016 – 12 Months
Biological light harvesting complexes are fascinating model systems for understanding excited state energy transport. Their high efficiency, coupled with their well defined and varied crystal structures makes them popular among those trying to find a path to the rational design of molecular electronics, photovoltaics and sensors. This project, begun in October 2015 within the group of Prof. Alessandro Troisi investigated the excited state energetic landscape of three biological light-harvesting systems, the Fenna-Matthews-Olsen complex, Light Harvesting II Complex and the Peridinin Chlorophyll Protein. We developed and applied a diabatisation method which would provide a universal description of the quantum and semiclassical effects involved in coupling the excited states of chromophores to produce a total description of the excitonic Hamiltonian of the proteins.
DSM Speciality Resins – 2014-2015 – 12 Months
Consumers and companies increasingly seek to move away from petrochemically sourced chemicals, as these feedstocks run low, we must be prepared to make the jump to bio-renewable chemicals. This one-year synthetic research project investigated the development of novel bio-renewable monomers for water-dispersed composite polyurethanes alongside Prof. Cor Koning (TUE Eindhoven).