Agency: GTR | Branch: EPSRC | Program: | Phase: Training Grant | Award Amount: 3.94M | Year: 2014
The achievements of modern research and their rapid progress from theory to application are increasingly underpinned by computation. Computational approaches are often hailed as a new third pillar of science - in addition to empirical and theoretical work. While its breadth makes computation almost as ubiquitous as mathematics as a key tool in science and engineering, it is a much younger discipline and stands to benefit enormously from building increased capacity and increased efforts towards integration, standardization, and professionalism. The development of new ideas and techniques in computing is extremely rapid, the progress enabled by these breakthroughs is enormous, and their impact on society is substantial: modern technologies ranging from the Airbus 380, MRI scans and smartphone CPUs could not have been developed without computer simulation; progress on major scientific questions from climate change to astronomy are driven by the results from computational models; major investment decisions are underwritten by computational modelling. Furthermore, simulation modelling is emerging as a key tool within domains experiencing a data revolution such as biomedicine and finance. This progress has been enabled through the rapid increase of computational power, and was based in the past on an increased rate at which computing instructions in the processor can be carried out. However, this clock rate cannot be increased much further and in recent computational architectures (such as GPU, Intel Phi) additional computational power is now provided through having (of the order of) hundreds of computational cores in the same unit. This opens up potential for new order of magnitude performance improvements but requires additional specialist training in parallel programming and computational methods to be able to tap into and exploit this opportunity. Computational advances are enabled by new hardware, and innovations in algorithms, numerical methods and simulation techniques, and application of best practice in scientific computational modelling. The most effective progress and highest impact can be obtained by combining, linking and simultaneously exploiting step changes in hardware, software, methods and skills. However, good computational science training is scarce, especially at post-graduate level. The Centre for Doctoral Training in Next Generation Computational Modelling will develop 55+ graduate students to address this skills gap. Trained as future leaders in Computational Modelling, they will form the core of a community of computational modellers crossing disciplinary boundaries, constantly working to transfer the latest computational advances to related fields. By tackling cutting-edge research from fields such as Computational Engineering, Advanced Materials, Autonomous Systems and Health, whilst communicating their advances and working together with a world-leading group of academic and industrial computational modellers, the students will be perfectly equipped to drive advanced computing over the coming decades.
Velez S.,CIC Nanogune
Nature Photonics | Year: 2016
Plasmons in graphene nanoresonators have many potential applications in photonics and optoelectronics, including room-temperature infrared and terahertz photodetectors, sensors, reflect arrays or modulators. The development of efficient devices will critically depend on precise knowledge and control of the plasmonic modes. Here, we use near-field microscopy between λ0 = 10–12 μm to excite and image plasmons in tailored disk and rectangular graphene nanoresonators, and observe a rich variety of coexisting Fabry–Perot modes. Disentangling them by a theoretical analysis allows the identification of sheet and edge plasmons, the latter exhibiting mode volumes as small as 10-8λ0 3. By measuring the dispersion of the edge plasmons we corroborate their superior confinement compared with sheet plasmons, which among others could be applied for efficient 1D coupling of quantum emitters. Our understanding of graphene plasmon images is a key to unprecedented in-depth analysis and verification of plasmonic functionalities in future flatland technologies. © 2016 Nature Publishing Group Source
Corsetti F.,CIC Nanogune
Computer Physics Communications | Year: 2014
The implementation of the orbital minimization method (OMM) for solving the self-consistent Kohn-Sham (KS) problem for electronic structure calculations in a basis of non-orthogonal numerical atomic orbitals of finite-range is reported. We explore the possibilities for using the OMM as an exact cubic-scaling solver for the KS problem, and compare its performance with that of explicit diagonalization in realistic systems. We analyze the efficiency of the method depending on the choice of line search algorithm and on two free parameters, the scale of the kinetic energy preconditioning and the eigenspectrum shift. The results of several timing tests are then discussed, showing that the OMM can achieve a noticeable speedup with respect to diagonalization even for minimal basis sets for which the number of occupied eigenstates represents a significant fraction of the total basis size (>15%). We investigate the hard and soft parallel scaling of the method on multiple cores, finding a performance equal to or better than diagonalization depending on the details of the OMM implementation. Finally, we discuss the possibility of making use of the natural sparsity of the operator matrices for this type of basis, leading to a method that scales linearly with basis size. © 2013 Elsevier B.V. All rights reserved. Source
Agency: Cordis | Branch: H2020 | Program: MSCA-IF-EF-ST | Phase: MSCA-IF-2015-EF | Award Amount: 170.12K | Year: 2017
Graphene plasmons (GPs) enable the transport and control of light on an extreme subwavelength scale as well as the dynamic tunability via electric-gate voltage, which can be exploited for numerous applications such as for strong light-matter interactions, tunable infrared biosensing and absorption spectroscopy, subwavelength optical imaging, as well as for the development of tunable transformation optics devices, metamaterials and metasurfaces. However, electric GP tuning still has the limitations that could hinder potential applications. First, the electric-gate tuning of GPs is a volatile method, i.e. the tuned states of GPs cannot be kept without the applied bias. Consequently, GP electro-optic devices like plasmonic switches cannot provide the storable on- and off- states for low-energy-consuming signal control and processing. Second, the electric-gate tuning is usually slow, which cannot switch or modulate the GPs in an ultrafast time scale. In this proposal, we want to demonstrate that switchable phase change materials can offer a simple way to circumvent those two limitations and provide GPs with non-volatile, ultrafast and all-optical switching functionalities. These new functionalities would significantly enhance the application potential of GPs in the fields of optical sensing, all-optical plasmonic signal processing including modulation, switching and computing, and memory and digital metasurface and metamaterials.
Agency: Cordis | Branch: H2020 | Program: MSCA-IF-EF-ST | Phase: MSCA-IF-2014-EF | Award Amount: 158.12K | Year: 2015
Topological quantum computation (TQC) deals with the transformations related to the overall shape (topology) of a quantum trajectory to perform operations on data and go beyond the limitations of quantum computation. It is a revolutionary technique because it will allow quantum operations to be error free and robust while taking advantage of the radically new approaches of quantum computation, which means smaller systems, less energy dissipation, and faster processing. TQC may be naturally implemented using atomic scale systems such as those created by atomic manipulations with scanning probe techniques. The present project is to lay the foundations to make TQC possible. These foundations are the discovery of new exotic states of matter by developing the science and technology of 1-D chains of magnetic moments on superconductors. This implies multi-disciplinary training in single-molecule chemistry, fabrication of superconducting materials, atomic scale magnetic devices and quantum-computation principles. The project is aimed at creating unique career perspectives by learning skills in the atomic engineering of topological superconductors which will grant Dr. Choi a leading independent position. Intersectoral secondments will be used to explore industry interest in developing TQC as a high-value added technology.