PhD Studentship: Uncertainty quantification for machine learning models of chemical reactivity

CTI Clinical Trial and Consulting Services; Nottingham; On-site; GBP 21,000; Full time

Job summary

An innovative opportunity awaits in a forward-thinking research group focused on integrating AI with chemistry. This PhD project aims to enhance the accuracy of AI predictions in chemical reactivity, bridging the gap between human chemists and machine learning. The successful candidate will leverage a wealth of data from high-level quantum chemical calculations, exploring various machine learning algorithms to improve predictions. This role promises to contribute to greener chemistry by identifying efficient and sustainable methods for molecular synthesis. Join us in this exciting journey to revolutionize the field of chemistry through advanced technology.

Qualifications

• Strong background in Chemistry or Mathematics required.
• Experience with machine learning and programming essential.

Responsibilities

• Develop and implement techniques for estimating uncertainty in AI predictions.
• Evaluate machine learning algorithms for chemical reactivity.

Skills

Computer Programming

Machine Learning Algorithms

Quantum Chemical Calculations

Education

2:1 Honours Degree in Chemistry or Mathematics

MChem/MSc-4-year Integrated Masters

BSc with substantial research experience

Tools

ai4green Electronic Lab Notebook

Job description

Area

Chemistry

Location

UK Other

Closing Date

Monday 05 May 2025

Reference

SCI3039

In this PhD project, we will develop and implement approaches for estimating the uncertainty in AI predictions of chemical reactivity, to help strengthen the interaction between human chemists and machine learning algorithms and to assess when AI predictions are likely to be correct and when, for example, first principles quantum chemical calculations might be helpful.

Predicting chemical reactivity is, in general, a challenging problem and one for which there is relatively little data, because experimental chemistry takes time and is expensive. Within our research group, we have a highly automated workflow for high-level quantum chemical calculations and we have generated thousands of examples relating to the reactivity of molecules for a specific chemical reaction. This project will evaluate a variety of machine learning algorithms trained on these data and, most crucially, will develop and implement techniques for computing the uncertainty in the prediction.

The algorithms developed in the project will be implemented in our ai4green electronic lab notebook, which is available as a web-based application: http://ai4green.app and which is the focus of a major ongoing project supported by the Royal Academy of Engineering. The results of the project will help chemists to make molecules in a greener and more sustainable fashion, by identifying routes with fewer steps or routes involving more benign reagents.

Applicants should have, or expected to achieve, at least a 2:1 Honours degree (or equivalent if from other countries) in Chemistry or Mathematics or a related subject. A MChem/MSc-4-year integrated Masters, a BSc + MSc or a BSc with substantial research experience will be highly advantageous. Experience in computer programming will be essential. The studentship is open to home students only. The deadline for a formal application is 5 th May. Start date: 1 st Oct 2025. Annual tax-free stipend based on the UKRI rate (currently £20,780) plus fully-funded PhD tuition fees for the 3.5 years.

Supervisors: Jonathan Hirst (School of Chemistry), Simon Preston (Mathematical Sciences).

For further details and to arrange an interview please contact Jonathan Hirst (School of Chemistry).

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