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TETY Theoretical and Computational Materials Science Materials Theory Photonic, Phononic and Meta- Materials C. Soukoulis I. Remediakis G. Kopidakis M. Kafesaki (to be appointed) Materials Theory Group (est. 2007) C. Motsanos, N. Galanis, C. Mathioudakis, G. Kopidakis, I.Remediakis, E. Tylianakis, G. Barmparis, S. Stamatiadis (not shown: G. Kwtsopoulou, A. Maniadaki, G. Vantarakis, E. Pantoulas (graduated), K. Moratis (graduated)) Members: two faculty (I.R, G.K), one adjunct (C.M), five students (four PhD, one undergraduate), one staff. Training Core courses (programming, solid-state physics, quantum mechanics). Advanced courses (group theory, electronic structure). ~ 1 diploma thesis/year. 4 PhD students, 1 graduated. 2 ‘Manasaki’ best graduate student awards. From atomistic Simulations Electronic Structure Theory... Empirical Force Fields plus Classical Monte-Carlo and Molecular Dynamics Simulations. Quantum mechanical simulations (Tight-binding / LCAO). Ab initio simulations (Density-functional Theory - DFT). Variety of home-made, commercial and open-source codes running on a Beowulf cluster of ~60 nodes. … to computer-aided Design of new Materials Surface chemistry and catalysis. Carbon-based materials and other superhard ceramics. Quantum dots, nanocrystals, nanowires. Non-linear dynamics, energy localization and transfer. All-optical signal processing and firewalls. Hydrogen storage. Atomistic simulations We are usually interested in the ground and metastable states of the system, i.e. the global and local minima of G= U+ PV-TS= f(R1, R2, ...,RN; P, T, ...). Two tasks: (a) approximate G (b) minimize G. If U is more important than S (e.g. solids), we need an accurate quantum mechanical method (such as Density Functional Theory, DFT). Most CPU time is spent on calculation of G. If S is more important than U, (e.g gases and liquids), we need an accurate statistical method (such as empirical potential Monte Carlo or Molecular Dynamics). Most CPU time is spent on minimization of G (time evolution). Nano is different Gold is noble ...but nano-gold is a superb catalyst. Left: Jewel from Malia, Crete, Greece (ca. 1800 BC); Right: CO oxidation on Au nanoparticle (Remediakis, Lopez, Nørskov, Angew. Chem. (2005)). See also: “Making Gold Less Noble”, Mavrikakis et al., Catal. Lett. (2000). Nanoparticle shapes G = Gb u l k + Σ γh k l Ah k l (Gibbs, 1878) Equilibrium shape: minerals (billions of years to equilibrate) or nanopart icles (small size). www.mindat.org Turner et al., Adv. Func. Mater. 2009 Surface energies of Ru from DFT Virtual catalyst for NH3 synthesis Operation of this catalyst is a pure nano-effect. K. Honkala, A. Hellman, I. N. Remediakis, A. Logadottir, A. Carlsson, S. Dahl, C.H. Christensen and J. K. Nørskov, Science, 307 558 (2005); Surf. Sci., 600, 4264 (2006); Surf. Sci., 603, 1731 (2009). E=0.000 Si quantum dots in a-SiO2 E=0.010 E=0.010 Red : {100} Blue : {110} Green : {121} G. Hadjisavvas, I. N. Remediakis, P. C. Kelires, Phys. Rev. B 74, 165419 (2006); E=0.010 E=0.061 E=0.005 E=0.050 On-going collaboration with R. Kalia and P. Vashishta, USC. Shape of diamond nanocrystals in amorphous Carbon G. Kopidakis, I. N. Remediakis, M. G. Fyta and P. C. Kelires, Diam. Rel. Mater. 16 , 1875 (2007). Au nanoparticles in CO gas G. D. Barmparis & I. N. Remediakis, in preparation. TETY Theoretical and Computational Materials Science http://theory.materials.uoc.gr Theory and modeling in materials physics • Understand and control properties of materials with fundamental and practical interest from the bottom up by developing and using atomic-scale computational and theoretical tools • Simple models for fundamental understanding – General physical phenomena of wide applicability – Novel concepts of general validity – Qualitative results • Realistic models for accurate predictions – Atomistic computer simulations well suited for applications at nanoscale – Direct comparison with experiments • Current activities – Nonlinear wave localization and propagation – Structural, mechanical, electronic, optical properties of amorphous and nanostructured materials – Practical applications in ICT, “green” technologies Localization in nonlinear disordered systems • Widely used toy models in condensed matter (polarons, excitons) nonlinear optics, photonics, BECs Results often confirmed by realistic calculations • Discrete linear models – Periodic (homogeneous lattices) propagation – Disordered (inhomogeneous) Anderson localization • Discrete nonlinear models – Periodic, localization without disorder – Disordered ? GK, Aubry PRL 2000 • Interplay of disorder and nonlinearity – Mathematical and numerical results – Experimental confirmation Lahini et al PRL 2008 Localization in isolated nonlinear disordered systems • Anderson localization not destroyed by nonlinearity GK, Komineas, Flach, Aubry PRL 2008, Johansson, GK, Aubry EPL 2010 Propagation in driven nonlinear disordered systems Johansson, GK, Lepri, Aubry EPL 2009 Transmission thresholds for amplitude of driving field Self-induced transparency Targeted transfer of nonlinear excitations • Understand and control propagation phenomena in complex systems • Ultrafast electron transfer in photosynthetic reaction centers not thermally activated, nonlinear dynamical theory Biomimetics Aubry, GK JBP 2005 Amorphous and nanostructured carbon • Relate macroscopic properties and experiment to atomic bonding through simulation • Tight-binding molecular dynamics More efficient than first principles, more accurate than empirical potential calculations • Atomic structure, mechanical, electronic, optical properties Mathioudakis, GK, Kelires, Wang, Ho PRB 2004 Amorphous and nanostructured carbon Accurate calculation of imaginary part of dielectric function Mathioudakis, GK, Patsalas, Kelires DRM 2007 Nanodiamond in a-C • link atomic level structure with optoelectronic response Vantarakis, Mathioudakis, GK, Wang, Ho, Kelires PRB 2009 Diamond, a-D Density sp3 fraction 3.24 g/cm3 88% 2.91 g/cm3 71% 2.58 g/cm3 51% Mechanical properties of nanocrystalline materials • Hall-Petch effect for metals Hardness and yield strength increase with decreasing grain size • ‘Reverse’ Hall-Petch Softening when grain size is in nanometer range • Optimum grain size for strongest material Crossover from dislocation-dominated plasticity to grain-boundary sliding • dependence of elastic properties on grain size? Softening not limited to plastic deformations. • What about non-metals? Softening for non-metals, such as diamond. wikipedia Mechanical properties of nanocrystalline materials • Universal laws for softening of nanocrystalline materials – Emerge from our studies of elastic response of very different materials, such as copper and diamond. – Appear to be general, independent of chemical composition of material. – Derived from general considerations of increasing fraction of grain boundary atoms. Galanis, Remediakis, GK PSS 2010 Mechanical properties of nanocrystalline materials • Similar softening for ultra-nanocrystalline diamond Remediakis, GK, Kelires AM 2008 All-optical processing Optical transmission rates at hundreds Gb/s IP / Ethernet core Core routers Metro ring Electronic processors at a few Gb/s Bridge the gap by successfully implementing network security operations ‘on the fly’ No optical to electronic (and back) conversion R. Webb et al IEEE JSTQE 2011 Control signal Optical firewall Suspect packet Optical Optical routing bit switch filter Optical buffer memory Optical routing switch Intercept Pattern matching circuit Router Incoming data Optical Domain Firmware Interface Electronic Domain SAP Interface http://www.ist-wisdom.org/ General Purpose Processor External Collaborators S. Aubry Saclay, France M. Johansson Linkoping, Sweden K-M. Ho C-Z. Wang Ames, USA P. Kelires Lemessos, Cyprus J.K. Norskov Stanford, USA H. Hakkinen K. Honkala Jyvaskyla, Finland http://theory.materials.uoc.gr TETY Theoretical and Computational Materials Science http://theory.materials.uoc.gr http://theory.materials.uoc.gr 27