{"product_id":"computational-methods-for-large-systems-isbn-9780470487884","title":"Computational Methods for Large Systems","description":"While its results normally complement the information obtained by chemical experiments, computer computations can in some cases predict unobserved chemical phenomena Electronic-Structure Computational Methods for Large Systems gives readers a simple description of modern electronic-structure techniques. It shows what techniques are pertinent for particular problems in biotechnology and nanotechnology and provides a balanced treatment of topics that teach strengths and weaknesses, appropriate and inappropriate methods. It’s a book that will enhance the your calculating confidence and improve your ability to predict new effects and solve new problems.  Contributors xiii  \u003cp\u003ePreface: Choosing the Right Method for Your Problem xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA. DFT: The Basic Workforce 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1. Principles of Density Functional Theory: Equilibrium and Nonequilibrium Applications 3\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eFerdinand Evers\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Equilibrium Theories 3\u003c\/p\u003e \u003cp\u003e1.2 Local Approximations 8\u003c\/p\u003e \u003cp\u003e1.3 Kohn-Sham Formulation 11\u003c\/p\u003e \u003cp\u003e1.4 Why DFT Is So successful 13\u003c\/p\u003e \u003cp\u003e1.5 Exact Properties of DFTs 14\u003c\/p\u003e \u003cp\u003e1.6 Time-Dependent DFT 19\u003c\/p\u003e \u003cp\u003e1.7 TDDFT and Transport Calculations 28\u003c\/p\u003e \u003cp\u003e1.8 Modeling Reservoirs In and Out of Equilibrium 34\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2. SIESTA: A Linear-Scaling Method for Density Functional Calculations 45\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJulian D. Gale\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 45\u003c\/p\u003e \u003cp\u003e2.2 Methodology 48\u003c\/p\u003e \u003cp\u003e2.3 Future Perspectives 73\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3. Large-Scale Plane-Wave-Based Density Functional Theory: Formalism, Parallelization, and Applications 77\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eEric Bylaska, Kiril Tsemekhman, Niranjan Govind, and Marat Valiev\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 78\u003c\/p\u003e \u003cp\u003e3.2 Plane-Wave Basis Set 79\u003c\/p\u003e \u003cp\u003e3.3 Pseudopotential Plane-Wave Method 81\u003c\/p\u003e \u003cp\u003e3.4 Charged Systems 89\u003c\/p\u003e \u003cp\u003e3.5 Exact Exchange 92\u003c\/p\u003e \u003cp\u003e3.6 Wavefunction Optimization for Plane-Wave Methods 95\u003c\/p\u003e \u003cp\u003e3.7 Car – Parrinello Molecular Dynamics 98\u003c\/p\u003e \u003cp\u003e3.8 Parallelization 101\u003c\/p\u003e \u003cp\u003e3.9 AIMD Simulations of Highly Charged Ions in Solution 106\u003c\/p\u003e \u003cp\u003e3.10 Conclusions 110\u003c\/p\u003e \u003cp\u003e\u003cb\u003eB. Higher-Accuracy Methods 117\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4. Quantum Monte Carlo, Or, Solving the Many-Particle Schrödinger Equation Accurately While Retaining Favorable Scaling with System Size 119\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMichael D. Towler\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 119\u003c\/p\u003e \u003cp\u003e4.2 Variational Monte Carlo 124\u003c\/p\u003e \u003cp\u003e4.3 Wavefunctions and Their Optimization 127\u003c\/p\u003e \u003cp\u003e4.4 Diffusion Monte Carlo 137\u003c\/p\u003e \u003cp\u003e4.5 Bits and Pieces 146\u003c\/p\u003e \u003cp\u003e4.6 Applications 157\u003c\/p\u003e \u003cp\u003e4.7 Conclusions 160\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5. Coupled-Cluster Calculations for Large Molecular and Extended Systems 167\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eKarol Kowalski, Jeff R. Hammond, Wibe A. de Jong, Peng-Dong Fan, Marat Valiev Dunyou Wang, and Niranjan Govind\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 168\u003c\/p\u003e \u003cp\u003e5.2 Theory 168\u003c\/p\u003e \u003cp\u003e5.3 General Structure of Parallel Coupled-Cluster Codes 174\u003c\/p\u003e \u003cp\u003e5.4 Large-Scale Coupled-Cluster Calculations 179\u003c\/p\u003e \u003cp\u003e5.5 Conclusions 194\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6. Strong-Correlated Electrons: Renormalized Band Structure Theory and Quantum Chemical Methods 201\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eLiviu Hozoi and Peter Fulde\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 201\u003c\/p\u003e \u003cp\u003e6.2 Measure of the Strength of Electron Correlations 204\u003c\/p\u003e \u003cp\u003e6.3 Renormalized Band Structure Theory 206\u003c\/p\u003e \u003cp\u003e6.4 Quantum Chemical Methods 208\u003c\/p\u003e \u003cp\u003e6.5 Conclusions 221\u003c\/p\u003e \u003cp\u003e\u003cb\u003eC. More-Economical Methods 225\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7. The Energy-Based Fragmentation Approach for Ab Initio Calculations of Large Systems 227\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eWei Li, Weijie Hua, Tao Fang, and Shuhua Li\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 227\u003c\/p\u003e \u003cp\u003e7.2 The Energy-Based Fragmentation Approach and Its Generalized Version 230\u003c\/p\u003e \u003cp\u003e7.3 Results and Discussion 238\u003c\/p\u003e \u003cp\u003e7.4 Conclusions 251\u003c\/p\u003e \u003cp\u003e7.5 Appendix: Illustrative Example of the GEBF Procedure 252\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8. MNDO-like Semiempirical Molecular Orbital Theory and Its Application to Large Systems 259\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eTimothy Clark and James J. P. Stewart\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Basic Theory 259\u003c\/p\u003e \u003cp\u003e8.2 Parameterization 271\u003c\/p\u003e \u003cp\u003e8.3 Natural History or Evolution of MNDO-like Methods 278\u003c\/p\u003e \u003cp\u003e8.4 Large Systems 281\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9. Self-Consistent-Charge Density Functional Tight-Binding Method: An Efficient Approximation of Density Functional Theory 287\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMarcus Elstner and Michael Cous\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 287\u003c\/p\u003e \u003cp\u003e9.2 Theory 289\u003c\/p\u003e \u003cp\u003e9.3 Performance of Standard SCC-DFTB 300\u003c\/p\u003e \u003cp\u003e9.4 Extensions of Standard SCC-DFTB 302\u003c\/p\u003e \u003cp\u003e9.5 Conclusions 304\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10. Introduction to Effective Low-Energy Hamiltonians in Condensed Matter Physics and Chemistry 309\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eSen J. Powell\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Brief Introduction to Second Quantization Notation 310\u003c\/p\u003e \u003cp\u003e10.2 Hückel or Tight-Binding Model 314\u003c\/p\u003e \u003cp\u003e10.3 Hubbard Model 326\u003c\/p\u003e \u003cp\u003e10.4 Heisenberg Model 339\u003c\/p\u003e \u003cp\u003e10.5 Other Effective Low-Energy Hamiltonians for Correlated Electrons 349\u003c\/p\u003e \u003cp\u003e10.6 Holstein Model 353\u003c\/p\u003e \u003cp\u003e10.7 Effective Hamiltonian or Semiempirical Model? 358\u003c\/p\u003e \u003cp\u003e\u003cb\u003eD. Advanced Applications 367\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11. SIESTA: Properties and Applications 369\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMichael J. Ford\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Ethynylbenzene Adsorption on Au(111) 370\u003c\/p\u003e \u003cp\u003e11.2 Dimerization of Thiols on Au(111) 377\u003c\/p\u003e \u003cp\u003e11.3 Molecular Dynamics of Nanoparticles 384\u003c\/p\u003e \u003cp\u003e11.4 Applications to Large Numbers of Atoms 387\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12. Modeling Photobiology Using Quantum Mechanics and Quantum Mechanics\/Molecular Mechanics Calculations 397\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eXin Li, Lung Wa Chung, and Keiji Morokuma\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 397\u003c\/p\u003e \u003cp\u003e12.2 Computational Strategies: Methods and Models 400\u003c\/p\u003e \u003cp\u003e12.3 Applications 410\u003c\/p\u003e \u003cp\u003e12.4 Conclusions 425\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13. Computational Methods for Modeling Free-Radical Polymerization 435\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMichelle L. Coote and Chung Lin\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 435\u003c\/p\u003e \u003cp\u003e13.2 Model Reactions for Free-Radical Polymerization Kinetics 441\u003c\/p\u003e \u003cp\u003e13.3 Electronic Structure Methods 444\u003c\/p\u003e \u003cp\u003e13.4 Calculation of Kinetics and Thermodynamics 457\u003c\/p\u003e \u003cp\u003e13.5 Conclusion 468\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14. Evaluation of Nonlinear Optical Properties of Large Conjugated Molecular Systems by Long-Range-Corrected Density Functional Theory 475\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eHideo Sekino, Akihide Miyazaki, Jong-Won Song, and Kimihiko Hirao\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 476\u003c\/p\u003e \u003cp\u003e14.2 Nonlinear Optical Response Theory 478\u003c\/p\u003e \u003cp\u003e14.3 Long-Range-Corrected Density Functional Theory 480\u003c\/p\u003e \u003cp\u003e14.4 Evaluation of Hyperpolarizability for Long Conjugated Systems 482\u003c\/p\u003e \u003cp\u003e14.5 Conclusions 488\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15. Calculating the Raman and HyperRaman Spectra of Large Molecules and Molecules Interacting with Nanoparticles 493\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eNicholas Valley, Lasse Jensen, Jochen Autschbach, and George C. Schatz\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 494\u003c\/p\u003e \u003cp\u003e15.2 Displacement of Coordinates Along Normal Modes 496\u003c\/p\u003e \u003cp\u003e15.3 Calculation of Polarizabilities Using TDDFT 496\u003c\/p\u003e \u003cp\u003e15.4 Derivatives of the Polarizabilities with Respect to Normal Modes 500\u003c\/p\u003e \u003cp\u003e15.5 Orientation Averaging 501\u003c\/p\u003e \u003cp\u003e15.6 Differential Cross Sections 502\u003c\/p\u003e \u003cp\u003e15.7 Surface-Enhanced Raman and HyperRaman Spectra 506\u003c\/p\u003e \u003cp\u003e15.8 Application of Tensor Rotations to Raman Spectra for Specific Surface Orientations 507\u003c\/p\u003e \u003cp\u003e15.9 Resonance Raman 508\u003c\/p\u003e \u003cp\u003e15.10 Determination of Resonant Wavelength 509\u003c\/p\u003e \u003cp\u003e15.11 Summary 511\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16. Metal Surfaces and Interfaces: Properties from Density Functional Theory 515\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eIrene Yarovsky, Michelle J. S. Spencer, and Ian K. Snook\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Background, Goals, and Outline 515\u003c\/p\u003e \u003cp\u003e16.2 Methodology 517\u003c\/p\u003e \u003cp\u003e16.3 Structure and Properties of Iron Surfaces 521\u003c\/p\u003e \u003cp\u003e16.4 Structure and Properties of Iron Interfaces 538\u003c\/p\u003e \u003cp\u003e16.5 Summary, Conclusions, and Future Work 553\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17. Surface Chemistry and Catalysis from Ab Initio-Based Multiscale Approaches 561\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eCatherin Samofl and Simone Piccinin\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 561\u003c\/p\u003e \u003cp\u003e17.2 Predicting Surface Structures and Phase Transitions 563\u003c\/p\u003e \u003cp\u003e17.3 Surface Phase Diagrams from Ab Initio Atomistic Thermodynamics 568\u003c\/p\u003e \u003cp\u003e17.4 Catalysis and Diffusion from Ab Initio Kinetic Monte Carlo Simulations 576\u003c\/p\u003e \u003cp\u003e17.5 Summary 584\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18. Molecular Spintronics 589\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eWoo Youn Kim and Kwang S. Kim\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 589\u003c\/p\u003e \u003cp\u003e18.2 Theoretical Background 591\u003c\/p\u003e \u003cp\u003e18.3 Numerical Implementation 600\u003c\/p\u003e \u003cp\u003e18.4 Examples 604\u003c\/p\u003e \u003cp\u003e18.5 Conclusions 612\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19. Calculating Molecular Conductance 645\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eGemma C. Solomon and Mark A. Ratner\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 615\u003c\/p\u003e \u003cp\u003e19.2 Outline of the MEGF Approach 617\u003c\/p\u003e \u003cp\u003e19.3 Electronic Structure Challenges 623\u003c\/p\u003e \u003cp\u003e19.4 Chemical Trends 625\u003c\/p\u003e \u003cp\u003e19.5 Features of Electronic Transport 630\u003c\/p\u003e \u003cp\u003e19.6 Applications 634\u003c\/p\u003e \u003cp\u003e19.7 Conclusions 639\u003c\/p\u003e \u003cp\u003eIndex 649  \u003c\/p\u003e \u003cb\u003eJEFFREY R. REIMERS, PhD\u003c\/b\u003e, is an Australian Research Council Professorial Research Fellow and works in the fields of molecular electronics and photosynthesis at The University of Sydney. Recently, he has been involved in the design and construction of single-molecule devices and has instituted a scanning-tunneling microscopy laboratory. Dr. Reimers has developed computational methods to solve problems involving strong electron-vibration coupling in biological photosynthesis, electron transport, and metal-organic chemistry.  \u003cp\u003eLearn how to choose and apply the best electronic structure methods to solve real-world problems in nanotechnology and biotechnology\u003c\/p\u003e \u003cp\u003eThere are a variety of computational methods to choose from to solve almost any electronic structure problem in nanotechnology and biotechnology, including problems involving complex systems with hundreds of thousands of atoms. This book presents the best and most useful of these computational methods, carefully explaining each one's strengths and weaknesses. Moreover, a broad range of practical applications are developed and then demonstrated with the use of detailed examples, helping you choose the best method for your particular needs.\u003c\/p\u003e \u003cp\u003eEach chapter of Computational Methods for Large Systems has been written by one or more leading experts in the development and application of computational methods. Chapters are logically organized into four parts:\u003c\/p\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ePart A, DFT: The Basic Workhorse, explores the use of density-functional theory (DFT) for performing electronic structure computations on ground and excited states of large biological, chemical, and physical systems.\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003ePart B, Higher Accuracy Methods, presents methods that can be used when modern DFT approaches don't work, including quantum Monte Carlo, coupled cluster calculations, and renormalized band-structure theory.\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003ePart C, More Economical Methods, examines methods such as semi-empirical DFT and Hartree-Fock-based approaches as well as empirical Hubbard models that enable researchers to work with larger systems at more approximate levels.\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003ePart D, Advanced Applications, applies electronic structure methods to nanoparticle and graphene structure, photobiology, control of polymerization processes, non-linear optics, nanoparticle optics, heterogeneous catalysis, spintronics, and molecular electronics.\u003c\/p\u003e \u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eWith extensive references to the primary literature, Computational Methods for Large Systems is an ideal reference for computational scientists as well as a text for graduate students in computational chemistry, physics, biochemistry, biotechnology, materials science, and nanoscience.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988966392037,"sku":"NP9780470487884","price":174.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470487884.jpg?v=1761782241","url":"https:\/\/k12savings.com\/es\/products\/computational-methods-for-large-systems-isbn-9780470487884","provider":"K12savings","version":"1.0","type":"link"}