Molecular scale machine-learning based modeling of dynamic fracture in rocks

In this project, you will use machine learning to develop and optimize molecular scale rock models that capture mechanical properties of rocks and rock–water interactions simultaneously. You will use these models to understand dynamic fracture of rocks and the role of water in rock fracture.


Dynamic fractures in rocks are associated with various important natural phenomena such as earthquakes. Fractures contribute to the energy budget during earthquake propagation, and knowing their behaviour all the way down to the nanoscale is needed to constrain such quantities as the stress drop and friction coefficient of the earthquake itself.

Brittle materials like rocks can often be described well by linear elastic fracture mechanics; the fracture properties come down to the elasticity of the material and a fracture toughness, both of which can be measured experimentally.

But rocks are often wet and heterogeneous, resulting in important deviations from the simplified rules of linear elastic fracture mechanics. Fracture propagation in brittle rocks is dynamic, and several interacting mechanisms may be at play simultaneously: A linear elastic-like fracture of the rock itself, weakening of the crack tip by water reacting with juvenile fracture surface, water phase transitions due to a sudden pressure drop during crack opening etc.

In this project, you will use machine learning to create molecular-scale models that capture both water–rock interactions and the structural and elastic properties of rocks simultaneously. These models will be used in molecular dynamics simulations to explore emergent properties of dynamic fractures in rocks both in the presence and absence of water.

Working with this project you will leverage machine learning as a tool for improving state-of-the-art simulation methods. This trains a computational science skill set that is applicable both in academia and in industry. The project may include collaborations with the Collaboratory for Advanced Computing and Simulations at University of Southern California.


  • MSc in physics or fracture mechanics or a mathematically focused MSc in geosciences.
  • Candidates with documented experience in statistical or computational physics, scientific programming, molecular dynamics simulations, and experience from machine learning will be prioritized.


Professor Anders Malthe-Sørenssen

Dr. Henrik Andersen Sveinsson

Call 2: Project start autumn 2022

This project is in call 2, starting autumn 2022. 



Published Sep. 21, 2020 4:55 PM - Last modified Oct. 29, 2021 9:59 AM