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Machine learning transition state geometries

   Centre for Accountable, Responsible and Transparent AI

   Applications accepted all year round  Competition Funded PhD Project (Students Worldwide)

About the Project

We have an exciting opportunity to work on the development of new transition state prediction methods. You will join the Grayson group in the Department of Chemistry to develop fast and accurate transition state geometry prediction tools by combining machine learning (ML) with reaction modelling for applications in pharmaceutical chemistry.

As a PhD student, you will:

·        Gain experience in the use of ML and reaction modelling for transition state geometry prediction.

·        Perform both individual and collaborative research projects.

·        Write up research results for publication in scientific journals.

·        Present your work at national and international conferences.

Project Details:

An unmet need in computational chemistry is the development of reaction modelling methodology that is fast and accurate and provides mechanistic insight from transition state (TS) geometries; existing modelling and machine learning (ML) methods compromise on at least one of these criteria. Such an approach to predicting reactivity would provide an alternative to costly experimental screening in synthetic route design and optimisation.

TS geometries are of particular importance in reaction design given their role in rationalising reaction outcomes, mechanistic elucidation, and improving the accuracy of ML reaction barrier prediction models in low data regimes. However, the standard approach to generating TSs with reaction modelling (e.g. quantum mechanical (QM) calculations) is highly time-consuming which prevents the screening of large numbers of reactions and conditions.

In this project, a novel approach to the rapid generation of QM-quality TS geometries using ML will be developed. These ML models will deliver high-quality TS geometries in seconds which is many orders of magnitude faster than QM. We will also build ML models that deliver fast and accurate reaction barrier predictions. Use of these TS and barrier prediction models will help eliminate experimental trial-and-error and provide a more rapid and sustainable approach to reaction discovery.

Further information about the Grayson group and our research interests can be found at and in references [1] and [2].

The project is part-funded by AstraZeneca and will include a placement of at least 3 months at their site in Macclesfield.

This project is associated with the UKRI Centre for Doctoral Training (CDT) in Accountable, Responsible and Transparent AI (ART-AI). We value people from different life experiences with a passion for research. The CDT's mission is to graduate diverse specialists with perspectives who can go out in the world and make a difference.

Applicants should hold, or expect to receive, a master's degree or first or upper-second bachelor's degree in a relevant subject. We are looking for a highly motivated individual to join our group. Experience with coding (any language) is desirable but not essential. Experience with machine learning and reaction modelling is not essential.

Formal applications should be accompanied by a research proposal and made via the University of Bath’s online application form. Enquiries about the application process should be sent to .

Start date: 2 October 2023.

Funding Notes

ART-AI CDT studentships are available on a competition basis and applicants are advised to apply early as offers are made from January onwards. Funding will cover tuition fees and maintenance at the UKRI doctoral stipend rate (£17,668 per annum in 2022/23, increased annually in line with the GDP deflator) for up to 4 years.
We also welcome applications from candidates who can source their own funding.


[1] Machine learning activation energies of chemical reactions. Wiley Interdiscip. Rev. Comput. Mol. Sci. 2022, 12, e1593.
[2] Machine learning and semi-empirical calculations: A synergistic approach to rapid, accurate, and mechanism-based reaction barrier prediction. Chem. Sci., 2022, 13, 7594.

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