PhD Studentship in Computational Chemistry - Modelling Stable Isotope Fractionation in Minerals: Computational Chemistry and Machine Learning

Supervisors: Dr. Ricardo Grau-Crespo (Chemistry, Reading – main supervisor), Dr. Zuowei Wang (Maths and Statistics, Reading), Dr. Marco Sacchi (Chemistry, Surrey), Prof. Dominik Fleitmann (Basel, Switzerland)

Project overview: We are looking for a motivated PhD student to work on a project combining computational chemistry and machine learning techniques to model stable-isotopic exchange between minerals and aqueous solutions, a geochemical process important for the reconstruction of climate history. For example, by comparing the ratio of oxygen isotopes in shells found in marine sediments, it is possible to determine the seawater temperature over time, because the isotope ratio is affected by temperature. The interpretation of isotope records requires a detailed understanding of the complex processes governing isotope exchange, which has motivated the development of theoretical models to predict and rationalise the fractionation of stable isotopes between different phases. Molecular modelling techniques based on quantum chemistry or classical forcefields, can be used to predict the fractionation of stable isotopes between phases. Significant progress has been achieved in recent years in this research direction, but some important challenges remain, because drastic approximations must often be made to avoid the huge computational cost of atomistic-level simulations. In this project, we will take advantage of recent major advances in molecular modelling, based on the incorporation of machine-learning algorithms, to overcome these limitations and achieve a faster and more accurate prediction of stable isotope fractionation. Drawing on the team’s experience modelling carbonate minerals with geochemistry applications in mind [1-3] and in applying machine-learning algorithms to accelerate computational chemistry simulations [4-7], we will develop new efficient methods for isotope fractionation prediction with the potential to transform the interpretation of stable-isotope records.

Eligibility:

• Applicants should hold or expect to gain a minimum of a 2:1 Bachelor Degree or equivalent in Chemistry, Physics, Materials Science, Computer Science, or closely related subjects.
• Due to restrictions on the funding this studentship is open to UK applicants.

Funding details:

• Starts September 2024.
• 3-year award, with options for extension of up to one more year.
• Funding covers full tuition fees, research expenses, plus UKRI stipend (currently £18,622 for 2023/24 academic year).

How to apply:  

To apply click bit.ly/ReadingPhDApply and create your account, and use the link sent by email to start the application process.   During the application process please select the PhD in Chemistry.

*Important notes*

• 1) Please quote the reference DRC24-004 in the ‘Scholarships applied for’ box which appears within the Funding Section of your on-line application.
• 2) If the application system prompts you to submit a research proposal, please paste in the project title and move on to the next step in the application.

Further Enquiries:

Please note that, where a candidate is successful in being awarded funding, this will be confirmed via a formal studentship award letter; this will be provided separately from any Offer of Admission and will be subject to standard checks for eligibility and other criteria. 

For further details please contact Dr Ricardo Grau-Crespo ([email protected] ).