Phase Transitions in 2D Materials and Thin Films using Machine Learning
Phase transitions in 2D materials and thin films are often distinct from those in bulk counterparts, owing to the altered interactions in reduced dimensions. For example, in bulk metallic transition metal dichalcogenides (TMD) the charge density wave (CDW) phase can exist, which is characterised by oscillations in the charge density that has a longer periodicity than the underlying lattice. If monolayers of the TMDs are isolated, the CDW phase can either be stabilised, as seen through higher transition temperatures relative to the normal state, destabilised, or even vanish entirely, depending on the chemistry of the TMD. Moreover, the sensitivity of these phase transitions to environmental factors, such as strain, doping, applied fields, supporting substrates can further alter these phases in low dimensions. Alternatively, intercalation of ions into bilayer graphene appears to have in-plane staging transitions, but intercalation into graphite is known to be dominated by out-of-plane staging. Further understanding these phase transitions in reduced dimensions is key for applications in energy and information storage materials.
Understanding these phase transitions requires first principles methods to achieve the desired accuracy, as electronic information is often key to the interactions driving these transitions. These methods can be extremely expensive, however, especially if large systems are required, or if a large number of external parameters need to be swept over. Recently, machine learning (ML) interatomic potentials and tight binding models have become accurate and data-efficient enough to be able to simulate materials with the same accuracy as first principles methods, but they can be scaled to massive systems and detailed phase diagrams can be constructed from being able to finely tune external parameters.
In this project we will investigate phase transitions in low-dimensional materials using machine learning methods. The phase transitions that we will study have some structural change associated with them which can be captured using a ML interatomic potential. These phase transitions can be further understood and compared against experiments from developing ML tight binding models to investigate the electronic properties of the large systems. For example, the CDW phase can be probed experimentally through scanning tunnelling microscopy, which measures the local density of states.
Any questions concerning the project can be addressed to Dr Zac Goodwin (zac.goodwin@materials.ox.ac.uk) or Prof Rebecca Nicholls (rebecca.nicholls@materials.ox.ac.uk).
General enquiries on how to apply can be made by e mail to graduate.studies@materials.ox.ac.uk. You must complete the standard Oxford University Application for Graduate Studies. Further information and an electronic copy of the application form can be found at https://www.ox.ac.uk/admissions/graduate/applying-to-oxford.
