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HetSys PhD Projects 2020

Name Description
Adaptive probabilistic meshless methods for evolutionary systems This project will develop and implement a new class of numerical solvers for evolving systems such as interacting fluid-structure flows. To cope with extreme strain rates and large deformations these new solvers will be adaptive and meshless, and they will also implicitly represent their own solution uncertainty, thus enabling optimal design and uncertainty quantification. This exciting project brings together aspects of continuum mechanics, numerical methods for partial differential equations, and statistical machine learning.
Atomistic modelling of fracture for irradiated materials Reactor pressure vessel (RPV) steels used in nuclear power plants have very complex behaviour due to the large number of alloying elements. Irradiation effects affect the flow of impurities towards grain boundaries, modifying solute segregation and leading to embrittlement and reduced operational performance. This project is part of a European consortium developing a multiscale model for embrittlement. The PhD project targets one of the last remaining gaps within the multiscale modelling of irradiated materials: linking neutron irradiation to variation of mechanical properties. The model will be validated by experiments carried out by partners at EDF and CEA (both in Paris).
Computational Modelling of Leidenfrost Fractals The Leidenfrost effect levitates small liquid drops above hot surfaces via a strong evaporative flux and is seen when water droplets skate across a hot pan. Understanding when this occurs is critical for the efficiency of numerous technologies, including the spray-cooling of next-generation electronics that our collaborators at Nokia Bell Labs are developing. Experiments at Bell have recently revealed counter-intuitive contact dynamics that display fractal surface wetting in competition with the nanoscale vapour flow under impacting droplets. This project will use unconventional mathematical modelling combined with computational techniques to gain unprecedented insight into this phenomenon combined with theory-driven targeted experiments at Bell.
Investigating the impact of equation of state uncertainties on direct-drive inertial fusion energy simulations Direct-drive inertial fusion energy1 (IFE) requires high-energy lasers to be focussed on a spherical target. The outer material of the target (usually plastic) ablates, driving an implosion of the core deuterium-tritium (DT) fuel. To design efficient future experiments and interpret previous ones an accurate and predictive computational modelling capability is required. This must include a formal understanding of the sources and magnitudes of uncertainty. This project will investigate the uncertainty in direct-drive IFE calculations arising from the equations-of-state used. The primary outcome of the project will be an uncertainty quantification (UQ) framework that could also be applied to other areas of uncertainty, such as opacity, emissivity and thermal transport.
It’s all in the Structure: Transforming drug design by bringing together molecular simulations and machine learning The solubility of pharmaceutical drugs determines to what extent they can be absorbed. Machine learning algorithms can predict the solubility of novel drugs without the need of actually synthetizing them - thus saving substantial time and money. However, we currently infer solubility from the structure of single molecules in vacuum - a sub-optimal approach ignoring interatomic interactions. This project, supported by AstraZeneca, will address this pitfall by generating three-dimensional molecular models of crystalline drugs polymorphs and simulate their dissolution by means of enhanced sampling simulations. These results will be used to construct a machine learning framework that will unravel the atomistic origins of drugs solubility.
Modelling the extraordinary strength of superalloys The extraordinary strength of superalloys (used e.g. in aeroplane engines) is caused by nanoscale precipitates formed in an ageing process. This process covers timescales from femtoseconds up to seconds and beyond, which poses a formidable modelling challenge. Isolating rare events where atoms actually move from thermal vibrations around their equilibrium position speeds up the simulation to allow studying the precipitate formation process with a view to understanding and potentially improving it. Of particular interest is the robustness of the predicted precipitation pathways to uncertainties in the atomistic model used. This project is co-funded by our industrial partner TWI.
Nanoscale material discovery for thermoelectric energy harvesting and cooling This project aims to exploit electronic and vibrational properties of nanoscale materials that are 1000 times smaller than diameter of a human hair to discover new materials for energy harvesting and cooling in consumer electronics such as mobile phones and laptops. The ultimate goal of the research is to understand quantum and phonon transport through molecular structures for thermoelectricity and thermal management. The candidate will receive a broad training on computational materials modelling and gain experience with cutting edge quantum transport simulation methods, conduct a vibrant research with publication potential and would have an opportunity to conduct collaborative projects with internationally leading experimental groups in Europe and beyond. For more information contact Dr. H Sadeghi ( or visit
Next generation sampling of organic molecules Models of simple flexible organic molecules, such as methane or carbon dioxide, have surprisingly rich phase diagrams showing numerous (and sometimes spurious) stable and metastable crystal structures. These models are of utmost interest in areas from organic electronics to pharmaceuticals. Traditional computer modelling of these systems requires laborious application of multiple techniques. We will instead adapt a novel “one shot” data-intensive algorithm (nested sampling) which makes both structural and thermodynamic predictions, to flexible organic molecules. We will use the resulting tool to automatically calculate full phase diagrams, initially for simple “toy” alkane models and ultimately realistic heterogeneous organic systems.
Predicting long-term materials ageing using reaction discovery and machine learning Predicting the long-term (decades or more) stability of organic polymeric materials under ambient environmental photothermal conditions is a unique challenge because experimental testing on such time-scales is often impossible or too expensive. In this project, we will merge reaction discovery tools (Habershon group) with machine-learning energy calculation methods (Maurer Group) to develop kinetic models to predict the long-term behaviour of organic polymeric materials. These predictive models will then have potential to be used to guide the choice of materials with tailored properties for long-term environmental applications.
Statistics of porous media attributes and mixing processes state variables across scales Typical observations of porous media attributes arise from a variety of techniques which have their own spatial resolution and associated uncertainty. Therefore, key statistics of parameters driving transport processes in porous media vary across scales and a scientific foundation for the characterization of basic transport mechanisms requires understanding of all relevant processes across the relevant length and time scales. In this project, we develop a theoretical and computational framework to assimilate data associated with diverse variables collected at a range of scales and combine these to provide predictions of solute dynamics and associated uncertainties.
Step into the unknown: modelling titanium alloys at extreme conditions Titanium alloys are very popular in industrial and medical applications due to their excellent mechanical and chemical properties. Among these the ternary alloy containing 6% aluminium and 4% vanadium is the most commonly used, yet little is known about the microscopical mechanisms that stabilise the alloy. Lack of insight makes it challenging to predict properties at extreme conditions, such as high pressures and temperatures near the melting point. To perform realistic computer simulations on the atomistic scale probing the uncharted territory of the phase diagram, you will develop a machine-learning accelerated model and apply it in large scale calculations.
Uncertainty in phase diagram simulations with interatomic potentials Atomistic simulations with interatomic potentials are very widely used throughout computational chemistry, physics and materials science. Currently many important processes are beyond the reach of quantum mechanical methods such as density functional theory; only empirical potentials can reach the necessary microstructural length scales and extended time scales. Currently it is almost impossible to put meaningful error bars on the output of complex atomistic simulations. This PhD project will address this challenge by relating simulation outcomes to the form and parameters of the potential, in collaboration with partners Ralf Drautz (ICAMS, Bochum, Germany) and Ryan Elliot (OpenKIM project, U. Minnesota, USA).