Other available projects
These projects fall outside of the HetSys training programme but are based at Warwick University and feature some cross over of supervisors and departments:
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.
For more details on this School of Engineering PhD Scholarship starting in 2024,click here.
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.
For more details on this PhD scholarship in the Department of Chemistry & Department of Physics starting in 2024, click here.
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.
For more details on this PhD scholarship in the Department of Chemistry & Department of Physics starting in 2024,click here.