Oliver Cunningham

Current Research:

Machine Learning for Computational Water Treatment

Using computational methods to investigate the interaction of Endocrine Disrupting Chemicals (EDCs) with adsorbents in aqueous environments. Molecular Dynamics (MD) simulations can be used to model the interaction between EDCs and solid surfaces in water. These simulations allow the calculation of system properties, such as the free energy of adsorption. 

The classical description of the system can be extended to include quantum effects through the use of Density Functional Theory (DFT) for small systems. Machine learning can be employed to train interatomic potentials that describe how the systems interact, utilising DFT calculations to achieve quantum mechanical accuracy with lower computational cost. 

The research focuses on using the properties of these systems as machine learning descriptors, alongside a large database of MD simulations of various EDCs and adsorbents, to train a model capable of predicting the best adsorbent for a given EDC.

Research Interests:

• Molecular Dynamics
• Machine Learning
• Aqueous Systems
• Water Treatment

Pure Profile:

https://pure.qub.ac.uk/en/persons/ollie-cunningham 

Supervisors:

Dr David Wilkins (Primary) and Dr Debra Phillips (Secondary)