The modelling of radiation damage in metals using Ehrenfest dynamics

In this thesis we use a time-dependent tight-binding model metal evolving under semiclassical Ehrenfest dynamics to explore the effects of electron-ion energy exchange on radiation damage phenomena. By incorporating an explicit model of quantum mechanical electrons coupled to a set of classical ions, our model correctly reproduces the interaction of excited ions with cooler electrons and captures phenomena absent in classical molecular dynamics simulations and in much-used analytical models. With our simple model we have been able to simulate large numbers of radiation damage cascades. We have directly explored the electronic excitations stimulated in such cascades and have found them to be well characterized by an elevated electronic temperature. We have also analysed the effect of these excitations in weakening the bonding interactions in our model metal, and the effect of these weakened interactions on the evolution of replacement collision sequences. By separating out components of the Hellmann-Feynman forces exerted by the electrons on the ions, we have identi ed the non-adiabatic force, resulting from the finite response time of the electrons to ionic motion and responsible for the accumulating electronic excitations. Based on simplifying physical arguments we have derived a temporallyand spatially-local expression for this force suitable for incorporation within a classical MD code at very low computational cost. Data from our simulations show that our new expression for the non-adiabatic force captures much of the microscopic detail of the direction and magnitude of the force. We find that it significantly outperforms commonly used viscous damping models of ion-electron energy transfer. At higher energies, our simulations of ion channelling reveal a new resonant enhancement of the electronic charge on the channelling ion and corresponding effects on the stopping force. We explain these phenomena with reference to the detailed atomic and electronic structure of our model

The Interaction of Proton Irradiation with Zr Textured Microstructure

Proton irradiation is commonly used as a surrogate for neutrons in experimental studies of structural materials for thermal reactors, such as Zr alloys. The proton beam is unidirectional which requires a choice to be made about the direction of the beam relative to the sample. The direction of the proton beam will determine what lattice orientations the protons will predominantly interact with, particularly in a sample with a strong texture. Since protons can be channelled the orientation of the crystal will affect the energy deposition and the implantation range. We have therefore investigated the degree of high energy proton channelling in crystal orientations found in a strongly textured sample of Zr. We find that the crystal orientations near low-index crystal directions like the D 1 1 2 0 E zone axis encourage a higher degree of proton channelling compared with other crystal orientations commonly found in a split-basal textured sample. Channelling in these orientations can change the energy deposition of protons by 40%. Our results show that care must be taken when quantifying the damage in textured samples using a single grain orientation. To compensate for channelling effects, the damage from ion irradiation in a textured sample should be quantified by averaging the damage across many different grain orientations, especially when the irradiation temperature of the bulk is less than 300 K

Digital Fingerprinting of Microstructures

Finding efficient means of fingerprinting microstructural information is a critical step towards harnessing data-centric machine learning approaches. A statistical framework is systematically developed for compressed characterisation of a population of images, which includes some classical computer vision methods as special cases. The focus is on materials microstructure. The ultimate purpose is to rapidly fingerprint sample images in the context of various high-throughput design/make/test scenarios. This includes, but is not limited to, quantification of the disparity between microstructures for quality control, classifying microstructures, predicting materials properties from image data and identifying potential processing routes to engineer new materials with specific properties. Here, we consider microstructure classification and utilise the resulting features over a range of related machine learning tasks, namely supervised, semi-supervised, and unsupervised learning. The approach is applied to two distinct datasets to illustrate various aspects and some recommendations are made based on the findings. In particular, methods that leverage transfer learning with convolutional neural networks (CNNs), pretrained on the ImageNet dataset, are generally shown to outperform other methods. Additionally, dimensionality reduction of these CNN-based fingerprints is shown to have negligible impact on classification accuracy for the supervised learning approaches considered. In situations where there is a large dataset with only a handful of images labelled, graph-based label propagation to unlabelled data is shown to be favourable over discarding unlabelled data and performing supervised learning. In particular, label propagation by Poisson learning is shown to be highly effective at low label rates

It bends but would it break?:topological analysis of BGP infrastructures in Europe

The Internet is often thought to be a model of resilience, due to a decentralised, organically-grown architecture. This paper puts this perception into perspective through the results of a security analysis of the Border Gateway Protocol (BGP) routing infrastructure. BGP is a fundamental Internet protocol and its intrinsic fragilities have been highlighted extensively in the literature. A seldom studied aspect is how robust the BGP infrastructure actually is as a result of nearly three decades of perpetual growth. Although global black-outs seem unlikely, local security events raise growing concerns on the robustness of the backbone. In order to better protect this critical infrastructure, it is crucial to understand its topology in the context of the weaknesses of BGP and to identify possible security scenarios. Firstly, we establish a comprehensive threat model that classifies main attack vectors, including but non limited to BGP vulnerabilities. We then construct maps of the European BGP backbone based on publicly available routing data. We analyse the topology of the backbone and establish several disruption scenarios that highlight the possible consequences of different types of attacks, for different attack capabilities. We also discuss existing mitigation and recovery strategies, and we propose improvements to enhance the robustness and resilience of the backbone. To our knowledge, this study is the first to combine a comprehensive threat analysis of BGP infrastructures withadvanced network topology considerations. We find that the BGP infrastructure is at higher risk than already understood, due to topologies that remain vulnerable to certain targeted attacks as a result of organic deployment over the years. Significant parts of the system are still uncharted territory, which warrants further investigation in this direction

Development of data-driven spd tight-binding models of Fe—parameterisation based on QSGW and DFT calculations including information about higher-order elastic constants

From IOP Publishing via Jisc Publications RouterHistory: received 2021-06-09, oa-requested 2021-08-26, rev-recd 2021-09-03, accepted 2021-09-22, epub 2021-10-20, open-access 2021-10-20, ppub 2021-12Publication status: PublishedFunder: Engineering and Physical Sciences Research Council; doi: https://doi.org/10.13039/501100000266; Grant(s): EP/P003591/1Abstract: Quantum-mechanical (QM) simulations, thanks to their predictive power, can provide significant insights into the nature and dynamics of defects such as vacancies, dislocations and grain boundaries. These considerations are essential in the context of the development of reliable, inexpensive and environmentally friendly alloys. However, despite significant progress in computer performance, QM simulations of defects are still extremely time-consuming with ab-initio/non-parametric methods. The two-centre Slater–Koster (SK) tight-binding (TB) models can achieve significant computational efficiency and provide an interpretable picture of the electronic structure. In some cases, this makes TB a compelling alternative to models based on abstraction of the electronic structure, such as the embedded atom model. The biggest challenge in the implementation of the SK method is the estimation of the optimal and transferable parameters that are used to construct the Hamiltonian matrix. In this paper, we will present results of the development of a data-driven framework, following the classical approach of adjusting parameters in order to recreate properties that can be measured or estimated using ab-initio or non-parametric methods. Distinct features include incorporation of data from QSGW (quasi-particle self-consistent GW approximation) calculations, as well as consideration of higher-order elastic constants. Furthermore, we provide a description of the optimisation procedure, omitted in many publications, including the design stage. We also apply modern optimisation techniques that allow us to minimise constraints on the parameter space. In summary, this paper introduces some methodological improvements to the semi-empirical approach while addressing associated challenges and advantages

Supernova Properties from Shock Breakout X-rays

We investigate the potential of the upcoming LOBSTER space observatory (due circa 2009) to detect soft X-ray flashes from shock breakout in supernovae, primarily from Type II events. LOBSTER should discover many SN breakout flashes, although the number is sensitive to the uncertain distribution of extragalactic gas columns. X-ray data will constrain the radii of their progenitor stars far more tightly than can be accomplished with optical observations of the SN light curve. We anticipate the appearance of blue supergiant explosions (SN 1987A analogs), which will uncover a population of these underluminous events. We consider also how the mass, explosion energy, and absorbing column can be constrained from X-ray observables alone and with the assistance of optically-determined distances. These conclusions are drawn using known scaling relations to extrapolate, from previous numerical calculations, the LOBSTER response to explosions with a broad range of parameters. We comment on a small population of flashes with 0.2 < z < 0.8 that should exist as transient background events in XMM, Chandra, and ROSAT integrations.Comment: 14 pages, 9 figures, accepted by MNRAS, presented at AAS 203rd meetin