Unravelling the intertwined correlated states of matter in moiré superlattices

The recent observation of many quantum correlated phases, including superconductivity and correlated insulating states, in twisted bilayers of 2D materials, has sparked tremendous interest and boosted intense research activity to understand these phases.

The ability of robustly engineering quantum states of matter with few tunable experimental knobs, e.g. the twist angle between the two 2D materials, represents a major breakthrough of these so-called moir materials, which started the new field of twistronics.

In particular, moir materials made from transition metal dichalcogenides (TMDs), have gained significant momentum as a novel and robust platform for simulating quantum phases of matter on emergent 2D lattices.

While it is widely accepted that these quantum phases are driven by enhanced electron-electron interactions in moir materials, the quantum nature of many correlated phases is still poorly understood.

Theoretical and computational first principles methods can be extremely powerful in helping to unravel the experimental signatures of the different quantum phases and also predict new ones.

However, standard methods, like density functional theory, are computationally too costly for moir systems (for which typical unit cells contain thousands of atoms) and generally unable to tackle the challenges posed by strongly correlated materials.

A new approach is therefore required.In this fellowship, I will develop an efficient multi-scale framework, involving different theoretical and computational methods, for studying quantum phases of TMDs moir superlattices.

Specifically, I will combine classical force field calculations, machine-learning based tight-binding methods and many-body methods to overcome the limitations of conventional first-principles approaches while maintaining their predictive power.This framework will allow us to shed light on the nature of the quantum phases hosted in moir materials, which can be harnessed in future technologies.