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  Modelling stable isotope fractionation in minerals: computational chemistry and machine learning


   School of Chemistry & Food

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  Dr R Grau-Crespo, Dr Marco Sacchi  No more applications being accepted  Funded PhD Project (UK Students Only)

About the Project

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. Such approaches are typically based on molecular-level considerations: the vibrational behaviour of atoms depends on the isotopic masses involved, affecting equilibrium free energies and kinetic barriers. Molecular modelling techniques based on quantum chemistry or classical forcefields, can then 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 algoritms 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. For enquiries please contact Dr Ricardo Grau-Crespo ([Email Address Removed]). 


Chemistry (6) Computer Science (8) Environmental Sciences (13) Physics (29)

Funding Notes

This project is suitable for students with a background in chemistry, physics or materials science, and with interest with theory and computation.

References

"1. SD Midgley, JO Taylor, DO Fleitmann, R Grau-Crespo. Chemical Geology 553 (2020) 119796.
2. SD Midgley, D Di Tommaso, D Fleitmann, R. Grau-Crespo. ACS Earth and Space Chemistry 5 (2021) 2066−2073.
3. SD Midgley, D Fleitmann, R Grau-Crespo. Geochimica et Cosmochimica Acta 324 (2022) 17-25.
4. LM Antunes, R Grau-Crespo, KT Butler. npj Computational Materials 8 (2022) 44.
5. SD Midgley, S Hamad, KT Butler, R Grau-Crespo. Journal of Physical Chemistry Letters 12 (2021) 5163-5168.
6. JJ Plata, V Posligua, A Marquez, JF Sanz, R Grau-Crespo. Chemistry of Materials 34 (2022) 2833–2841.
7. LM Antunes, KT Butler, R Grau-Crespo. Machine Learning Science and Technology 4 (2022) 015037."

Where will I study?

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