Big data and machine learning for sustainable synthesis

The Centre for Sustainable Chemical Technologies (CSCT) at the University of Bath has launched a joint PhD programme with Monash University, Australia.

This project is one of a number that are in competition for up to four funded studentships. More details are available here: http://www.csct.ac.uk/study-with-us/

Home institution: Bath University

Supervisor at Bath: Dr Matthew Grayson (lead)
Supervisor at Monash: Prof David Lupton

Context

The synthesis of bespoke molecules is essential in meeting the global demand for new agrochemicals, consumer products, materials and pharmaceutical drugs. Experimental trial-and-error has historically dominated reaction discovery but more cost effective, rapid and sustainable alternatives are becoming increasingly sought after. Density functional theory (DFT) calculations have been widely used post-experiment to explain observed reactivity and selectivity which enables the rational design of new reactions and helps to reduce the need for trial-and-error in reaction discovery. However, the computational design of new reactions pre-experiment is regarded as one of the “Holy Grails” of computational chemistry as high-accuracy DFT calculations are much slower than experimental screening methods; a typical computational mechanistic study can take 2-6 months. Faster computational methods can be used to study catalytic transformations but lack the accuracy needed to design reactions.

Big data and machine learning offer new opportunities for the computational design of reactions. Machine learning models, once trained, can make predictions for previously unseen molecules in seconds. This study will train machine learning models that can predict the outcomes of organocatalytic reactions important in pharmaceutical and polymer chemistry in a fraction of the time it would take to calculate them with DFT. Our work will provide the foundations for a new high-throughput in silico approach to designing and optimising reactions.

Use of these models for selected important enantioselective transformations will lead to a reduction in experimental trial-and-error and thus a more sustainable approach to reaction discovery. Furthermore, these models could be used to optimise atom economy which aligns with the second principle of circular chemistry (maximize atom circulation, Nat. Chem. 2019, 11, 190).

Application process

We invite applications from Science and Engineering graduates who have, or expect to obtain, a first or upper second class degree and have a strong interest in Sustainable & Circular Technologies.

You MUST express interest for three projects in order of preference – you can see all projects here: https://www.csct.ac.uk/bath-monash-global-phd-programme/ . Please submit your application at the Home institution of your preferred project (‘Home’ institution details can be found in the project summary). However, please note that you are applying for a joint PhD programme and applications will be processed as such.

University of Bath

Please submit your application through the following link: https://www.csct.ac.uk/bath-monash-global-phd-programme/
Please make sure to mention in the “finance” section of your application that you are applying for funding through the joint Bath/Monash PhD programme for your specified projects.
In the “research interests” section of your application, please name the three projects you are interested in and rank them in order of preference. Please also include the names of the Bath lead supervisors.

Monash University

Expressions of interest (EoI) can be lodged through https://www.monash.edu/science/bath-monash-program. The EoI should provide the following information:
CV including details of citizenship, your Official Academic Transcripts, key to grades/grading scale of your transcripts, evidence of English language proficiency (IELTS or TOEFL, for full requirements see: https://www.monash.edu/graduate-research/faqs-and-resources/content/chapter-two/2-2), and two referees and contact details (optional). You must provide a link to these documents in Section 8 using Google Drive (Instructions in Section 8).