A mechanism-led approach to reaction prediction

Background

The ab initio prediction of chemical behaviour has long been a holy grail for synthetic chemists as it will allow the targeting of the most efficient pathways to prepare high value commodity compounds. Preparing both new and known compounds in a timely and efficient manner is still a bottleneck for the chemical industry and considerable time and precious resources are routinely wasted on unsuccessful synthetic chemistry. This problem is exacerbated when the point of failure is close to the end of a multi-step synthetic route. The routine prediction of chemical reaction outcomes will therefore greatly accelerate the delivery of, for example, new pharmaceuticals and agrochemicals. However, such prediction is challenging as reaction outcomes depend on the interplay between a large number of factors, from the fundamental properties of the reagents, through to the reaction conditions employed. This project is focussed on developing new methods to predict the outcome of reactions based on synthetic and mechanistic studies, closely coupled with computational chemistry.

Objectives

The principal objectives of this project are to develop a comprehensive model to predict the outcome of industrially important reactions. We wish to develop a tool that will all but guarantee the outcome of organic and inorganic reactions prior to laboratory work. The ultimate objective is to then use this insight to develop new, efficient, synthetic chemistry.

Experimental Approach

The work will involve studying both organic and inorganic reactions mechanisms through a suite of different spectroscopic methods (e.g UV-vis, IR and NMR spectroscopy). Analysis of these experimental results will provide a rich dataset to build mechanistic and computational models (primarily using density functional theory) to understand the key factors controlling reaction outcomes. The model will then be developed in an iterative fashion by testing predicted reaction outcomes and then developing the model based on the accuracy of the results.

Novelty and Training

The novelty of this programme lies in the coupling of mechanistic insight (i.e. rate constants, orders of reaction, identification of key intermediates) with computational chemistry to identify the factors which control the outcome of new chemical reactions. The student undertaking this project will be trained in state-of-the-art experimental mechanistic study to gain detailed understanding of the factors controlling reaction outcomes. There will also be an opportunity to develop additional skills, such as using computational chemistry to predict reaction outcomes, based on the experimental observations. All Chemistry research students have access to our innovative Doctoral Training in Chemistry (iDTC): cohort-based training to support the development of scientific, transferable and employability skills: https://www.york.ac.uk/chemistry/postgraduate/idtc/  

The Department of Chemistry holds an Athena SWAN Gold Award and is committed to supporting equality and diversity for all staff and students. The Department strives to provide a working environment which allows all staff and students to contribute fully, to flourish, and to excel: https://www.york.ac.uk/chemistry/ed/.

For more information about the project, click on the supervisor's name above to email the supervisor. For more information about the application process or funding, please click on email institution

This PhD will formally start on 1 October 2022. Induction activities may start a few days earlier.

To apply for this project, submit an online PhD in Chemistry application:

https://www.york.ac.uk/study/postgraduate/courses/apply?course=DRPCHESCHE3

You should hold or expect to achieve the equivalent of at least a UK upper second class degree in Chemistry or a related subject.  

Students with an interest in synthetic and mechanistic organic and inorganic chemistry are encouraged to apply.