PhD Studentship: Digital chemistry driven optimisation of catalytic chemical processes

Funding

EPSRC Doctoral Training Partnership Studentship offering the award of fees, together with a tax-free maintenance grant of £19,237 per year for 3.5 years.  Training and support will also be provided.

Lead Supervisor’s full name and email address

Professor Thomas Chamberlain – [email protected]

Co-supervisor name(s)

Professor Richard Bourne – [email protected]

Dr Adam Clayton – [email protected]

Project summary

The discovery of new catalytic systems and the subsequent reduction of the time scale for transfer of these new chemical processes to the point of reliable manufacture and entrance into the market place is critically important to the health of the nation. These processes contain a vast number of potentially critical parameters, making exploration problematic.  This is particularly complicated in the context of catalysis, because as well as continuous variables, such as reaction temperature, reaction time, which are typically easier to integrate into algorithmic optimisation approaches, discrete variables, such as metal and/or ligand type, play a pivotal role. To date integration of such parameters into optimisation experiments has been much less explored.

This project aims to develop a cyber physical platform approach revolutionizing the transfer from laboratory to production of multistep catalytic processes using advanced data-rich and cognitive computing technologies. We will exploit new algorithms based on Bayesian Optimisation for continuous and discrete variables and evolving methodologies for kinetic model determination that merge data analysis and the generation of further experiments. Machine learning software will generate experiment set points delivered through the cloud to automated laboratory platforms. Multiplexed, on line analytic techniques, including HPLC, GC, NMR, MS, IR etc., will enable analysis of reactions to inform further experiments, thus generating a data generation - data analysis closed-loop. This enables the application of machine learning to chemical development: the system will continuously learn, increasing in confidence and knowledge over time, from previous iterations.

Entry requirements plus any necessary or desired background

First or Upper Second Class UK Bachelor (Honours) degree or equivalent

Subject Area

Chemical Engineering, Synthetic Chemistry, Organic Chemistry, Pharmaceutical/Medicinal Chemistry