This project will investigate the structural and dynamical properties of liquid chromophores—molecules responsible for colour in natural and synthetic systems. The work will centre on developing machine-learning models that describe the intermolecular interactions between chromophores, enabling efficient simulations of their molecular motion over long timescales. These models aim to capture how chromophores organise and behave in the liquid state, where their dynamics are essential for understanding processes such as optical response, energy transfer, and molecular relaxation.
Neutron scattering will play a key role in the project. Inelastic neutron scattering (INS) and quasi-elastic neutron scattering (QENS) will be used to probe both vibrational and diffusive dynamics, while neutron-based structural measurements will provide direct information on intermolecular arrangements. These experimental data will also serve as an essential benchmark for validating the machine-learning models.
The project is expected to deliver a combined computational and experimental framework for understanding the behaviour of liquid chromophores, advancing both fundamental knowledge and modelling capabilities for complex molecular liquids.