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Calculation of the Properties of Chemically Active Nonequilibrium Plasma

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Calculation of the Properties of Chemically Active Nonequilibrium Plasma From atoms and molecules to new materials and technologies Predictive Modeling of Advance Materials and Material Processing Based on Multiscale Simulation Paradigms Boris Potapkin Kintech Laboratory Ltd Presented at International School on “Computer simulation of advanced materials” MSU, Moscow July 2012 Approach Standard approach to design of new materials and technologies: empirical search Material, Idea Prototype Testing technology ü Expensive ü Long time ü No assimilation of fundamental data The role of modeling consists in description and extrapolation of experimental data using phenomenological models Integrated approach, based on predictive modeling Material, Idea Modeling Prototype Testing technology ü Reduced time and cost of development ü Reduced risks A priory modeling, based on detail understanding of structures and mechanisms at atomistic level, before construction of prototype Technologies and tools of predictive modeling Problems ü Huge computers resources needed even for every single level EngineeringEngineering design,design, ü Unfeasibility of direct integration of spatial and temporal scales UnitUnitprocessprocess design design Direct integration ü No reliable integration methods FiniteFinite element element Analysis,Analysis, ü Stochastic nature of multilevel modeling: models, data, properties ContinuumContinuum modes modes MesoscaleMesoscale ü Cognitive problems ( data formats, computer languages etc.) modeling,modeling, MCMC Molecular Molecular Mechanism ü No effective software tools for scales integration and collaborative dynamicsdynamics (MD) work QuantumQuantum (MD) mechanicsmechanics (QC)(QC) Background for solution ü Exponential growth of computer resources (Moore’s law, HPC) ü Development of high-performance algorithms ü Development of predictive theoretical methods Solution Development of methods and tools for integrated multilevel modeling Elaboration and use of High Performance Computing algorithms and systems Multiscale Modeling for Advanced Materials Fuel Cells, Energy Electrochemical Organic Matrix Materials batteries Nano composites Metamaterials Focus on: Catalysts Materials Properties OLED, LED, PV, OPV Simulation Paradigms Macro level : FE, FDTD for Mesoscale methods: DPD, Mean Atomistic methods: quantum optics Field Models, MC chemistry, molecular dynamics, DFT High Performance Computing Code parallelization HPC Architecture choice Platform-specific Code Optimization (Tailoring) Information Technologies for Cloud Computing & Distributed Collaboration Kintech Lab profile KINTECH was founded in 1998 by scientists and engineers from and the NRC "Kurchatov Institute“, MIFI, and Moscow State University ACTIVITY FIELDS: ü Conducting of inventive research and consulting for a wide range of applications ü Software development for multi-scale multi-physics modeling KINTECH Lab, Ltd. modeling and design in complete cycle 1, Kurchatov Sq., Moscow, ü Customer support in their own research activity using the Russian Federation, advanced simulation capabilities of KINTECH's software (+7 (499) 196-78-37 7 +7 (499) 196-99-92 - [email protected] www.kintechlab.com Selected Kintech projects in energy and materials: materials & devices ü Multiscale modeling of thin high-k dielectric film deposition and the investigation of their properties ü First-principles modeling of defects at the SiC/SiO2 interface ü Multiscale modeling of the optic properties of metamaterials and the design of devices on their basis ü Multiscale modeling of semiconductor nanosensors and CdTe solar cells ü Multiscale modeling and screening of scintillator and phosphor materials ü Optimization of the fabrication of microelectromechanical (MEMS) devices ü Multiscale modeling of phase transition in ferroelectric materials ü Modeling of Nano Electro Mechanical systems (NEMS) based on carbon nanotubes ü First-principles modeling of catalysts for fuel cells ü Predictive modeling of carbon based materials Selected Kintech projects:projects in energy and materials : ü Multiscale modeling for PDE design ü Multiscale modeling of cold spay technology ü Multi-physics modeling of car exhaust cleaning ü Mechanistic modeling of depleted combustion processes ü Mechanistic modeling of coal gasification kinetic ü Plasma waste gasification modeling ü Industrial safety: explosions ü Multiscale modeling of chemically active plasma systems including PAC and plasma exhaust cleaning modeling ü Multiscale modeling of chemically active plasma systems ü Membrane gas separation modeling and system design ü First-principles modeling of catalysts for fuel cells ü Software development for environmental and industrial safety Kintech selected customers ü General Electric since 2003 ü Motorola (Freescale Semiconductor), 2000-2006 ü Intel since 2007 ü Siemens ü Daimler ü Qualcomm ü David System and Technology SL, Spain ü Scientific Utilization Inc. USA ü Renault, France ü Rhone Poulence, France ü TNO, Netherlands ü DLR (Germane Airspace Center), Germany ü Arvin Meritor Inc, USA. ü PlasmaSol Corp., USA ü Princeton University, USA Key Parallel Software tools Developed at Kintech Kintech Lab develops methods and special software tools for multilevel modeling in different engineering fields: üChemical Workbench – an integrated environment for the development and reduction of chemical mechanisms for combustion, plasma, etching, films growth üFDTD-II – a tool for modeling the optical properties of metamaterials üMD-kMC – an integrated environment for atomistic modeling üEtchLab – a tool for modeling and optimization of MEMS fabrication üTRACC - integrated package to solve 3D fluid dynamics problems with radiation transport using special software and a database Predictive multi scale modeling for energy and materials: specific projects KINTECH Lab, Ltd. 1, Kurchatov Sq., Moscow, Russian Federation, (+7 (499) 196-78-37 Advanced plasma light 7 +7 (499) 196-99-92 - [email protected] source www.kintechlab.com Theoretical Screening of Effective Emitting Substances in Fluorescent Plasma Light Sources Goal: mercury free light source development Emitting systems Number of metals - 54 Ligands number - 8 Problems Large number of candidates Number of system to explore: = 486 Advanced plasma sources of light Hg-free lamps on metal halides non-equilibrium plasma I (-) +M*=>I + e +M GaI3 + e =>GaI3(-)=>…. 2 2 Exp. GE spectrum =>…GaI+e.=>Ga + I,I2(-) j j Ga +e=>Ga*=> Ga + hw Рисунок лампы с травлением evaporation etching condensation Ga, GaI2, GaI3 (wall) GaI3(pellet) Ar : GaI3 New corrected set P (Ar cold) = 2 Torr, R = 1.27 cm,I = 300 mA 6 tube 1 1.0 3 T = 79 C ( [GaI ] = 3.3 1013 cm-3 ) GaI( p) GaI Dissociation by electron impact 3 through different dissociable terms 2 2 0.8 Ga( P)+I( P) 2 4 cm 0.6 GaI(A) -16 theory 0.4 2 b 10 Q(u), kXA AX 0.2 GaI(X) 0.0 Emission power, mW/cm 0 X 0 10 20 30 40 50 200 250 300 350 400 450 u, eV Wavelength (nm) Non-empirical multi physics multi scale approach to calculation of the properties of chemically active nonequilibrium plasma Plasma modeling for parameters Recommendations optimization and processes control Construction and analysis of a physical - chemical mechanism Ga(4p1/2)+GaI3=>GaI+GaI2 Ga(4p1/2)=>Ga(Wall) GaI=>GaI(Wall) GaI2=>GaI2(Wall) Evaluation of cross I=>I(Wall) Ga(4p3/2)=>Ga(Wall) Ga(4p1/2)+e=>Ga(5s)+e Ga(4p3/2)+e=>Ga(5s)+e sections, rate constants, Ga(4p1/2)+e=>Ga(4d)+e Ga(4p3/2)+e=>Ga(4d)+e GaI+e=>Ga(4p1/2)+I+e and radiation parameters GaI2+e=>GaI+I+e GaI3+e=>GaI+I2+e GaI+e=>Ga(4p3/2)+I+e -0.6 -0.4 -0.2 0.0 0.2 0.4 Sensitivity ab initio calculations of unknown parameters of atoms, molecules, and their interactions DATA ACQUISITION BY AB INITIO ELECTRONIC STRUCTURE CALCULATIONS During the last decades computational electronic structure methods have become Ø competitive to experimental techniques in the accuracy of excited state energies (0.1 eV and better) Ø often more accurate than experiment in transition probabilities, oscillator strengths Ø reliable source of information on the states & transitions difficult for experimental studies Highly accurate but expensive ab initio methods (coupled-cluster like) Intermediate accuracy approaches (e.g. multireference perturbation theories) Roughly approximate methods (e.g. time-dependent DFT) EXCITED ELECTRONIC STATES OF MOLECULES Highly accurate but expensive ab initio methods Approximate MultiReference Coupled Clusters = size- Exhaustive info on consistence corrected Small (2-3 non-H low-lying states of configuration interaction atoms) molecules small light-element (MR AQCC, ACPF) molecules Columbus, Asec 3 Excitation energies: Response / Green function < 0.1 eV errors techniques Dalton, Gaussian, Molpro Molecules with Cfour, Asec 3 simple shell Oscillator structure (closed- strengths: shell or one open Relativistic ± 10-20 % and shell) formulations for Fock-space coupled cluster better heavy-element methods compounds Asec 3 available EXCITED ELECTRONIC STATES OF MOLECULES Intermediate accuracy approaches Small & medium size Multiconfiguration molecules Scanning of perturbation theories (~101 non-H atoms) potential surfaces (PT): CASPT2 etc (fails in certain Molpro, Molcas areas) Firefly, Games Excitation energies: ~ 0.1 eV accuracy Multireference (effective-Hamiltonian) many-body Scanning of Perturbation Theory Oscillator strengths potential surfaces Relativistic (PT): ± 10-30 % (MCQDPT, & transition formulation of quasidegenerate PT MPPT) moments for states MPPT exists (MCQDPT) of any nature multipartitioning PT Etc Efop EXCITED ELECTRONIC STATES OF MOLECULES Roughly approximate
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