Neural Quantum States for Automated Discovery of Emergent Quantum Phases in Two-Dimensional Materials

About the Project

Two-dimensional quantum materials, in which reduced dimensionality, topology and strong electron-electron interactions can combine, are a promising platform for discovering new many-body phases. In twisted and layered van der Waals systems, these behaviours can be tuned through twist angle, strain and stacking, creating a rich landscape of potential correlated quantum phenomena. A central challenge is to build predictive discovery frameworks that connect realistic electronic structure to low-energy models, allowing robust identification of the resulting phases.

This PhD will develop an automated computational pipeline for the discovery and characterisation of correlated quantum phases in two-dimensional materials, with particular emphasis on twisted and layered heterostructures of 2D materials. The student will combine density functional theory (DFT), Wannier tight-binding models and continuum or moiré Hamiltonians to construct realistic material-specific models. The associated interacting many-body problem will then be solved using a neural quantum states (NQS) approach.

NQS are flexible variational wavefunction models in which the exponentially complex many-electron quantum state is represented compactly by a trainable neural network, making them a powerful route to scalable many-body simulation beyond conventional exact diagonalisation and mean-field approaches. The resulting pipeline will be used to predict energies, gaps, correlation functions and phase boundaries, and to rank candidate systems for phenomena such as interaction-driven insulating states, charge order, fractional Chern phases and other emergent quantum states. The broader goal is to establish a transferable discovery framework that connects ab-initio calculations, many-body simulation and automated phase identification across a wider class of low-dimensional quantum materials.

The project combines condensed-matter theory, electronic-structure modelling, machine learning and high-performance computing, and will suit applicants interested in quantum materials, many-body physics and scientific computing. It will be based at the National Graphene Institute, providing an excellent research environment at the interface of theory, computation and two-dimensional materials science. The student will benefit from close proximity to a broad community working on quantum materials, van der Waals heterostructures and advanced characterisation, with clear scope to apply the computational methods developed here in collaboration with experimental colleagues.

Eligibility

Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related discipline.

Funding

This DKO Fellowship award (3.5 years) is open to Home (UK) and overseas students. The successful candidate will receive an annual tax-free stipend set at the UKRI rate (£21,805 for 2026/27; subject to annual uplift), and tuition fees will be paid. We expect the stipend to increase each year. The start date is October 2026.

We recommend that you apply early as the advert may be removed before the deadline.