Computational discovery of new superconductors (see also Superconductivity at extreme pressures with Bartomeu Monserrat) - Chris Pickard
A long-standing dream is the discovery of superconducting materials that require little or no cooling at ambient conditions. For many years this search has been confined to the so-called unconventional superconductors. One of the first applications of ab intio random structure searching (AIRSS) was to investigate potential high temperature conventional superconductivity in the hydrogen rich compound, silane (SiH4), under pressure. The predicted structures were rapidly confirmed experimentally, and the computational hunt for other promising candidates began. A landmark result was the theoretcially guided experimental discovery of superconductivity at 204K in H3S. More recently we proposed that the rare earth hydrides might superconduct at approaching room temperature, and two experimental groups have confirmed this. The problem with all these results is that so far the superconducting hydrides require the application of extremely high pressures for their formation. The aim of this project is to use, and develop, state of the art computational techniques to search for high temperature conventional superconductors at low pressures.
Navigating materials structure space - Chris Pickard
The ability to picture the invisible is a key tool in the armoury of any scientist. In materials science we are faced with a particular challenge - how do deal with visualisation from the atomic scale, all the way up to the macroscopic. With an emphasis on data generated by computer models, this project will begin by surveying the state of the art in materials visualisation, and attempt to answer the following questions: Can we do better? How can we find the hidden gems in a mass of data - either in models containing millions of atoms, or in millions of models? How might emerging technologies (such as virtual reality) be used by materials scientists? The techniques explored might be those of persistent homology, complex network analysis, or others energing from the machine learning community.
Structure prediction of interfaces in two-dimensional materials - Chris Pickard
Two-dimensional materials have become of great importance and are widely studied both in terms of new physics as well as for device applications. Although much is known about the properties of many bulk two-dimensional materials, much less is known about the properties due to grain boundaries or at interfaces of two-dimensional materials. A better theoretical understanding of the properties of grain boundaries in polycrystalline two-dimensional materials, the properties of heterostructures of two-dimensional materials, or low dimensional electrical contacts, crucially relies on first finding their atomic structure. We apply the ab initio random structure searching (AIRSS) approach, previously primarily applied to bulk structure prediction, to structure prediction of interfaces in two-dimensional materials. The method can be applied to both grain boundary structures as well as investigating heterostructure interfaces. It is based on density functional theory, in combination with high-throughput computing. Many possible materials can be studied (for instance, graphene, h-BN, phosphorene).
Modelling and characterisation of Hydrogen-assisted failure in nuclear pressure vessel steels - Enrique Galindo-Nava
The nuclear industry generates a significant share of the global production of electricity, where safe operation is its highest priority. Major accidents in the past have been related to local hydrogen accumulation leading to sudden failure in structural components. This phenomenon is known as hydrogen embrittlement (HE) and results from the interactions of hydrogen with crystal defects in the microstructure, such as vacancies, dislocations, boundaries, and second phases. Reactor pressure vessels play a critical role in the safety of reactors and the materials used must be able to contain the reactor’s core at high pressures and elevated temperatures. Austenitic (face-centred cubic) stainless steels are used in the inner parts of pressure vessels due to their good corrosion resistance but they are prone to HE. Damage is promoted by the combination of high concentrations of H and deformation-induced phase transformations, but fundamental understanding is required to unravel how such interactions occur.
The research will consist in developing a multi-physics continuum modelling approach to prescribe the influence of the microstructure and phase transitions in the promotion of hydrogen embrittlement in austenitic stainless steels. The student will also learn advanced characterisation methods, mechanical testing, and thermal desorption spectroscopy to validate the models.
This project will offer the unique opportunity to work in close collaboration with Rolls-Royce, major manufacturer of nuclear reactors and aeroengines, as well as potentially receiving additional top-up to EPSRC’s basic stipend.
Modelling and experimentation of microstructure and Hydrogen embrittlement in welds of dissimilar materials - Enrique Galindo-Nava
Safe operation is a primary concern for next generation nuclear reactors and welded structures are amongst the most critical components for safety due to their susceptibility to hydrogen embrittlement. They need constant checking through the life of a reactor. The combination of welding and hydrogen embrittlement is an exciting and challenging problem in Materials Science due to the multi-physics involved in these processes. Laser welding of metals forms a melt-pool and the subsequent rapid solidification results in the modification of the microstructure and properties. In addition, hydrogen is absorbed during welding into the molten alloy but as the weld metal solidifies, the solubility of H decreases by the drop in temperature. H then diffuses away from the weld fusion zone to the heat-affected zone, where the microstructure is most heterogeneous and has residual stresses. These are ideal conditions for H-induced cracking but it it is not understood how such phenomena changes with material conditions.
The aim of this project is to understand the relationships between process parameters, microstructure and H behaviour in welds of dissimilar steels. This will require developing Thermodynamic and continuum models for microstructure evolution and link them with simulations for H diffusion and deformation. The results will be used to define algorithms for process optimisation. The work will also involve conducting advanced microscopic characterisation, mechanical testing and thermal desorption spectroscopy to validate the models.
This project will offer the unique opportunity to collaborate closely with Rolls-Royce, major manufacturer of nuclear reactors and aeroengines, as well as potentially receiving additional top-up to EPSRC’s basic stipend.
Phase field modelling of phase transitions in additively manufactured metallic alloys - Enrique Galindo-Nava
Additive Manufacturing in metals is an emerging technology for producing near-net shape components directly from computer-aided design data without using part-depending tools. However, outstanding challenges must be tackled before it can be widely commercialised. Most notable issues include material inhomogeneities and defect-related loss in mechanical properties. The microstructure is central to our understanding of how inhomogeneities and defects can be reduced. In allotropic materials, such as Ti and Fe alloys, the formation of undesired structures, including columnar grains and martensitic transformations due to very high solidification rates, has shown the strongest influence in property variability.
This project aims at understanding and quantifying the critical factors controlling the microstructure and properties in additive manufactured alloys, via phase field modelling and experimentation. This will involve building simulation algorithms capturing the cyclic variations in temperature and deformation conditions leading to phase transformations. The results will be used to optimise processing parameters for improved mechanical properties. The student will also be expected to conduct advanced microscopic characterisation and mechanical testing to validate the models to be developed.
The project will offer the opportunity of collaborating with other academic institutions and possibly industry.