Stable isotope fractionation in minerals as a tool for climate reconstruction: insights from molecular modelling

Subtle changes in the ratio of stable isotopes in natural climate archives such as sediments in oceans, polar ice caps or speleothems can be precisely measured to reconstruct climate history. For example, by measuring the ratio of oxygen isotopes in shells found in marine sediments, it is possible to determine changes in seawater temperature and global ice volume over time, because the isotope ratio is affected by both factors. The interpretation of isotope records requires a detailed understanding of the complex processes governing isotope exchange and fractionation, 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. While significant progress has been achieved in recent years in this research direction, some important challenges remain, because drastic approximations must often be made to avoid the huge computational cost of the atomistic-level simulations: e.g.: i) interatomic interactions are simplified; ii) isotope fractionations are predicted considering only the mineral bulk behaviour, neglecting the mineral-aqueous interface; and iii) non-equilibrium (kinetic) effects are usually ignored. In this project, we will take advantage of recent major advances in molecular modelling to overcome these limitations and achieve a faster and more accurate prediction of stable isotope fractionation, with the potential to transform the interpretation of isotope records. We will employ molecular dynamics simulations to model isotope exchange between minerals and aqueous solution, considering: i) accurate quantum-mechanical representation of interatomic interactions; ii) the effect of the mineral-aqueous interface and therefore of particle size on the overall fractionation ratios; iii) non-equilibrium and kinetic effects. To afford the drastic increase in computational cost that such improvements require, we will combine quantum chemistry with machine-learning (ML) techniques, where the latter are used to vastly accelerate the former without significant accuracy sacrifice.