Prediction and control of mechanical deformations are central concepts in the design of advanced materials. Yet, submicron materials reveal highly erratic mechanical response to applied stress and these fluctuations are attributed to dislocations and their collective behaviour. Grain boundaries as well play a key role in transmitting stress cross different grains. The phase field crystal model has a powerful and versatile approach to modelling a wide range of crystal-related phenomena, including mechanical deformations mediated by dislocations and grain boundary migration.
This project aims to combine theoretical and computational studies of plastic instabilities and stress fluctuations in crystalline systems using the phase field crystal approach. The candidate will develop a theoretical characterization of dislocations and grain boundaries. One of the computational goals is to simulate the phase field crystal model and study the statistics of stress fluctuations in single crystals and polycrystalline materials, where grain boundaries are important. The candidate will develop appropriate machine learning models to accelerate the phase field crystal simulations by leaping in time. The candidate will apply machine learning techniques, such as regression neural networks and support vector machine, to also analyse the predictability of yield stress. The candidate will have the opportunity to focus more on the theoretical or computational aspects of this project depending on his/her research background and skills.
Requirements
- MSc in physics, preferably in statistical, computational or condensed matter physics.
- Candidates with documented experience in scientific programming (solving non-linear PDE's, data analysis, finite element methods) or theoretical background in statistical physics will be prioritized.
Supervisors
Associate Professor Luiza Angheluta-Bauer
Professor Anders Malthe-Sørenssen
Call 2: Project start autumn 2022
This project is in call 2, starting autumn 2022.