Supervisory Team : Professor Chris-Kriton Skylaris
The aim of this PhD project is to develop, use and deploy software and workflow tools that will enable novel science at the interface of chemical separations and catalytic transformations.
Catalysis is an industry that is expected to be worth $35 billion annually by 2025 and touches every aspect of human life, from the production of commodity chemicals to clean energy such as biofuels and fuel cells, and of course chemical separations where the search for more efficient and environmentally benign catalysts is a very pressing need.
While there has been great progress in accurate quantum atomistic methods for simulating a given chemical reaction the crucial question of how to predict all the chemical reactions that will take place between a collection of molecules and a catalytic site is still an unsolved problem.
An additional under-represented aspect of efficient catalytic processes includes separations challenges that affect the desirable gradients, catalyst recycling or product recovery.
Towards addressing this problem, we will develop and demonstrate methods capable of simulating reactions that take into account both challenges.
For this we will use efficient methods such as the NWPEsSe program for Potential Energy Surface searches (PESs) for finding energy minima and methods exploring the PES for the chemical reactions that can take place.
To describe the energies of the catalysts and reactants we will use quantum chemistry approaches such as Density Functional Theory (DFT) and faster empirical methods such as Density Functional Tight-Binding (DFTB).
As the catalysts are often metallic nanoparticles supported on surfaces or membranes, the DFT calculations will include large numbers of atoms and will need to be performed with a linear-scaling DFT approach such as the ONETEP program which we develop in our group.
ONETEP also has advanced electrolyte and solvent modelling capabilities, which will allow the study of the chemical reactions under electrochemical conditions.
Descriptors will be derived from the simulations to capture the space of predictions of chemical separation paths and at later stages machine learning methods could be trained to generate the quantities required to speed up these calculations.
This PhD studentship is in collaboration with the Pacific Northwest National Laboratory (PNNL), USA who will provide periods of placement at their research laboratories.
The research will be jointly supervised by Professor Chris-Kriton Skylaris and Drs Vanda Glezakou and Roger Rousseau from PNNL.
The project will be based at the University of Southampton in the group of Professor Chris-Kriton Skylaris. Applicants should have a high-level degree in Chemistry, Physics or related subject and a strong interest in computational chemistry.
For further details please contact Professor Chris-Kriton Skylaris (c.skylaris soton.ac.uk).
A very good undergraduate degree (at least a UK 2 : 1 honours degree, or its international equivalent).
Closing date : 31 August 2021
Funding : For UK students, Tuition Fees and a stipend of £15,609 tax-free per annum for up to 4 years.