Dear colleagues,

I would like to bring to your attention a PhD position on computational mineralogy at the University of Oslo. The opening is part of a large interdisciplinary and interdepartmental CO-FUND team (36 PhD students on various computational topics) involved in the application of artificial intelligence and machine learning techniques to address current computational problems in natural sciences.
Please note that the deadline is March 1st.

Thank you for your help,
Sincerely,
Razvan Caracas

====

PhD position on hydrogen diffusion along dislocation planes in upper mantle silicates using machine-learning

Bulk diffusion of hydrogen in various materials received extreme attention over the last decades because of its fundamental role in a wide range of engineering, materials science, and natural processes. But the influence of the dislocations and grain boundaries on the diffusion paths is less well understood, though at least equally important.

Here we will study the diffusion of hydrogen along dislocation planes in mantle silicate crystals. We will use molecular dynamics simulations based on both ab initio forces and interatomic potentials generated with the help of machine learning techniques. We will explore the mechanism for hydrogen diffusion in the silicate minerals and investigate its impact on the overall dynamics of the Earth's mantle. 

Molecular water enters the Earth's mantle along the subduction zones and affects the state, history, and dynamics of the mantle. Water softens the host crystal structures, like those of silicates, enhances their electrical conductivity, and changes deformation patterns and the resulting textures under stress. It can play an extremely important role in the rheology of particular regions of the mantle. In extreme cases, hydrogen can even induce large-scale melting. 

Here we plan to explore the mechanisms, pathways, and associated coefficients for hydrogen diffusion within the two most abundant and relevant phases of the Earth - (Mg,Fe)₂SiO₄ and (Mg,Fe)SiO₃ at realistic conditions of temperature (up to 2000K), pressure (up to 40 GPa), and stress (up to GPa). The thesis involves a research internship in a partner laboratory at the University of Montpellier, where the results of the numerical calculations can be directly compared to experimental data.

To study diffusion we first employ molecular dynamics simulations based on ab initio techniques. They ensure high-quality data over a broad range of thermodynamic conditions. Then we fit advanced reactive interatomic potentials using machine-learning techniques to perform ultra-large-scale simulations.

Then we use the diffusion information to adjust the mantle flow models to constrain rates of water transfer from subducting slabs into the surrounding mantle, via both solid diffusion and transport via melt pockets. This will help us predict patterns of water storage in the mantle, and evaluate the influence of water on overall mantle dynamics.

Requirements

• MSc in physics, geophysics, material science, or related field.
• Candidates with documented experience in computational geophysics, molecular dynamics, ab initio simulations, and experience from machine learning will be prioritized.

Apply:

This project is run at the University of Oslo and is part of the CompSci doctoral program (https://www.mn.uio.no/compsci/english/projects/).
For more information, please contact Razvan Caracas ([email protected]).
The application deadline for the call 1 is March 1st 2021.

Please apply using the guide at:

https://www.mn.uio.no/compsci/english/application/

====

-- Dr. Razvan Caracas Senior Researcher Laboratoire de Géologie de Lyon CNRS, Ecole Normale Superieure de Lyon and Adjunct Professor The Center for Earth Evolution and Dynamics (CEED) University of Oslo https://razvancaracas.info/