Title and link

Recent advances in computational quantum field theory have led to non-perturbative approaches to predicting scattering amplitudes and correlation functions in quantum mechanics. This holds much promise for transforming elegant quantum field theories into the genuine predictive modelling of quantum systems, but the diffusive dynamics of industry-standard simulation algorithms generate highly correlated samples of the target distribution. This project will develop state-of-the-art simulation algorithms whose ballistic-style superdiffusive dynamics drive the system through its state space – rapidly generating samples with very low autocorrelation and paving the way to high-quality quantum predictions.

Computational design of topological defects in graphene

The nanomaterial graphene is ultrathin, ultra-strong and highly conductive. However, to find new real-world applications it must be tailored to a given function, e.g. to be more adhesive or to sense gases. Idealised graphene is a two-dimensional sheet of pure carbon. Its topology is defined by linked hexagonal rings of carbon; each hexagon perfectly identical to the next. By topologically designing defects to build graphene block by block with non-hexagonal rings, non-carbon atoms, or missing carbon atoms, we can controllably introduce new functionality to graphene that will enhance its applicability to support metal catalysts, to sense molecules, and to be used as components in nanoelectronic devices.

The goal of this PhD project is to develop and employ computational simulation and electronic structure theory approaches to identify how certain local defects can be created in two-dimensional graphene networks.

Depts. Chem. and Phys.

Integrating machine learning and multiscale modelling for simulating fracture in materials with uncertainties

Early detection of damage in materials is crucial, as cracks reduce local stiffness, can affect structural integrity, and accelerate the ageing process of physical assets. This project will help predict damage degradation in materials and enable mitigation measures to prevent potential failure of structural components, which are critical for ensuring safety and achieving societal objectives.

The aim of the project is to exploit the recent advances in machine learning (ML) and multiscale modelling for simulating damage in materials.

Machine learning guided design of efficient sustainable materials for hydrogenation photocatalysis

Fuel cells, photovoltaic devices, photocatalytic converters – they all are crucial elements in delivering decarbonization and sustainable energy production at a global scale within the coming decades. They all fundamentally involve energy transfer and chemical dynamics at interfaces where molecules, electrons, and light interact to deliver a certain function. The underlying mechanisms of ultrafast dynamics at surfaces triggered by light or electrons are not well understood, which, for example, limits our ability to design photocatalyst materials that deliver optimal light absorption, catalytic activity, and energy transport.

In this project, you will use newly developed machine learning surrogate models to simulate the light-driven promotion of CO hydrogenation to CHO on plasmonic catalyst materials, an important bottleneck reaction in syngas and CO2 reforming. The PhD project will establish the mechanistic details of hot electron interaction with CO and the key design parameters for optimal photocatalytic CO hydrogenation.

Depts. Chem. and Phys.

The need for energy sustainability and the environmental consequences of fossil fuels make the development of technologies for clean energy imperative. Thermoelectric (TE) materials can harvest enormous amounts of waste heat and convert it into useful electrical power. As 60% of all energy we use is lost into heat during conversion processes, the realization of efficient and scalable TEs can transform the energy-use/savings landscape and play a major role in net-zero sustainability. However, TEs have not found widespread use because of low material efficiencies.