{"product_id":"molecular-kinetics-in-condensed-phases-isbn-9781119176770","title":"Molecular Kinetics in Condensed Phases","description":"\u003cp\u003e\u003cb\u003eA guide to the theoretical and computational toolkits for the modern study of molecular kinetics in condensed phases\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eMolecular Kinetics in Condensed Phases: Theory, Simulation and Analysis\u003c\/i\u003e puts the focus on the theory, algorithms, simulations methods and analysis of molecular kinetics in condensed phases. The authors – noted experts on the topic – offer a detailed and thorough description of modern theories and simulation methods to model molecular events. They highlight the rigorous stochastic modelling of molecular processes and the use of mathematical models to reproduce experimental observations, such as rate coefficients, mean first passage times and transition path times.\u003c\/p\u003e \u003cp\u003eThe book’s exploration of simulations examines atomically detailed modelling of molecules in action and the connections of these simulations to theory and experiment. The authors also explore the applications that range from simple intuitive examples of one- and two-dimensional systems to complex solvated macromolecules. This important book:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eOffers an introduction to the topic that combines theory, simulation and analysis\u003c\/li\u003e \u003cli\u003ePresents a guide written by authors that are well-known and highly regarded leaders in their fields\u003c\/li\u003e \u003cli\u003eContains detailed examples and explanation of how to conduct computer simulations of kinetics. A detailed study of a two-dimensional system and of a solvated peptide are discussed.\u003c\/li\u003e \u003cli\u003eDiscusses modern developments in the field and explains their connection to the more traditional concepts in chemical dynamics\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eWritten for students and academic researchers in the fields of chemical kinetics, chemistry, computational statistical mechanics, biophysics and computational biology, \u003ci\u003eMolecular Kinetics in Condensed Phases \u003c\/i\u003eis the authoritative guide to the theoretical and computational toolkits for the study of molecular kinetics in condensed phases.\u003c\/p\u003e \u003cp\u003eAcknowledgments xiii\u003c\/p\u003e \u003cp\u003eIntroduction: Historical Background and Recent Developments that Motivate this Book xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 The Langevin Equation and Stochastic Processes \u003c\/b\u003e\u003cb\u003e1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 General Framework 1\u003c\/p\u003e \u003cp\u003e1.2 The Ornstein-Uhlenbeck (OU) Process 5\u003c\/p\u003e \u003cp\u003e1.3 The Overdamped Limit 8\u003c\/p\u003e \u003cp\u003e1.4 The Overdamped Harmonic Oscillator: An Ornstein–Uhlenbeck process 11\u003c\/p\u003e \u003cp\u003e1.5 Differential Form and Discretization 12\u003c\/p\u003e \u003cp\u003e1.5.1 Euler-Maruyama Discretization (EMD) and Itô Processes 15\u003c\/p\u003e \u003cp\u003e1.5.2 Stratonovich Discretization (SD) 17\u003c\/p\u003e \u003cp\u003e1.6 Relation Between Itô and Stratonovich Integrals 19\u003c\/p\u003e \u003cp\u003e1.7 Space Varying Diffusion Constant 21\u003c\/p\u003e \u003cp\u003e1.8 Itô vs Stratonovich 23\u003c\/p\u003e \u003cp\u003e1.9 Detailed Balance 23\u003c\/p\u003e \u003cp\u003e1.10 Memory Kernel 25\u003c\/p\u003e \u003cp\u003e1.11 The Many Particle Case 26\u003c\/p\u003e \u003cp\u003eReferences 26\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 The Fokker–Planck Equation \u003c\/b\u003e\u003cb\u003e29\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 The Chapman–Kolmogorov Equation 29\u003c\/p\u003e \u003cp\u003e2.2 The Overdamped Case 30\u003c\/p\u003e \u003cp\u003e2.2.1 Derivation of the Smoluchowski (Fokker–Planck) Equation using the Chapman–Kolmogorov Equation 30\u003c\/p\u003e \u003cp\u003e2.2.2 Alternative Derivation of the Smoluchowski (Fokker–Planck) Equation 33\u003c\/p\u003e \u003cp\u003e2.2.3 The Adjoint (or Reverse or Backward) Fokker–Planck Equation 34\u003c\/p\u003e \u003cp\u003e2.3 The Underdamped Case 34\u003c\/p\u003e \u003cp\u003e2.4 The Free Case 35\u003c\/p\u003e \u003cp\u003e2.4.1 Overdamped Case 35\u003c\/p\u003e \u003cp\u003e2.4.2 Underdamped Case 36\u003c\/p\u003e \u003cp\u003e2.5 Averages and Observables 37\u003c\/p\u003e \u003cp\u003eReferences 39\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 The Schrödinger Representation \u003c\/b\u003e\u003cb\u003e41\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 The Schrödinger Equation 41\u003c\/p\u003e \u003cp\u003e3.2 Spectral Representation 43\u003c\/p\u003e \u003cp\u003e3.3 Ground State and Convergence to the Boltzmann Distribution 44\u003c\/p\u003e \u003cp\u003eReferences 47\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Discrete Systems: The Master Equation and Kinetic Monte Carlo \u003c\/b\u003e\u003cb\u003e49\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 The Master Equation 49\u003c\/p\u003e \u003cp\u003e4.1.1 Discrete-Time Markov Chains 49\u003c\/p\u003e \u003cp\u003e4.1.2 Continuous-Time Markov Chains, Markov Processes 51\u003c\/p\u003e \u003cp\u003e4.2 Detailed Balance 53\u003c\/p\u003e \u003cp\u003e4.2.1 Final State Only 54\u003c\/p\u003e \u003cp\u003e4.2.2 Initial State Only 54\u003c\/p\u003e \u003cp\u003e4.2.3 Initial and Final State 55\u003c\/p\u003e \u003cp\u003e4.2.4 Metropolis Scheme 55\u003c\/p\u003e \u003cp\u003e4.2.5 Symmetrization 55\u003c\/p\u003e \u003cp\u003e4.3 Kinetic Monte Carlo (KMC) 58\u003c\/p\u003e \u003cp\u003eReferences 61\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Path Integrals \u003c\/b\u003e\u003cb\u003e63\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 The Itô Path Integral 63\u003c\/p\u003e \u003cp\u003e5.2 The Stratonovich Path Integral 66\u003c\/p\u003e \u003cp\u003eReferences 67\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Barrier Crossing \u003c\/b\u003e\u003cb\u003e69\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 First Passage Time and Transition Rate 69\u003c\/p\u003e \u003cp\u003e6.1.1 Average Mean First Passage Time 71\u003c\/p\u003e \u003cp\u003e6.1.2 Distribution of First Passage Time 73\u003c\/p\u003e \u003cp\u003e6.1.3 The Free Particle Case 74\u003c\/p\u003e \u003cp\u003e6.1.4 Conservative Force 75\u003c\/p\u003e \u003cp\u003e6.2 Kramers Transition Time: Average and Distribution 77\u003c\/p\u003e \u003cp\u003e6.2.1 Kramers Derivation 78\u003c\/p\u003e \u003cp\u003e6.2.2 Mean First Passage Time Derivation 80\u003c\/p\u003e \u003cp\u003e6.3 Transition Path Time: Average and Distribution 81\u003c\/p\u003e \u003cp\u003e6.3.1 Transition Path Time Distribution 82\u003c\/p\u003e \u003cp\u003e6.3.2 Mean Transition Path Time 84\u003c\/p\u003e \u003cp\u003eReferences 86\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Sampling Transition Paths \u003c\/b\u003e\u003cb\u003e89\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Dominant Paths and Instantons 92\u003c\/p\u003e \u003cp\u003e7.1.1 Saddle-Point Method 92\u003c\/p\u003e \u003cp\u003e7.1.2 The Euler-Lagrange Equation: Dominant Paths 92\u003c\/p\u003e \u003cp\u003e7.1.3 Steepest Descent Method 96\u003c\/p\u003e \u003cp\u003e7.1.4 Gradient Descent Method 97\u003c\/p\u003e \u003cp\u003e7.2 Path Sampling 98\u003c\/p\u003e \u003cp\u003e7.2.1 Metropolis Scheme 98\u003c\/p\u003e \u003cp\u003e7.2.2 Langevin Scheme 99\u003c\/p\u003e \u003cp\u003e7.3 Bridge and Conditioning 99\u003c\/p\u003e \u003cp\u003e7.3.1 Free Particle 102\u003c\/p\u003e \u003cp\u003e7.3.2 The Ornstein-Uhlenbeck Bridge 102\u003c\/p\u003e \u003cp\u003e7.3.3 Exact Diagonalization 104\u003c\/p\u003e \u003cp\u003e7.3.4 Cumulant Expansion 105\u003c\/p\u003e \u003cp\u003eReferences 111\u003c\/p\u003e \u003cp\u003eAppendix A: Gaussian Variables 111\u003c\/p\u003e \u003cp\u003eAppendix B 113\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 The Rate of Conformational Change: Definition and Computation \u003c\/b\u003e\u003cb\u003e117\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 First-order Chemical Kinetics 117\u003c\/p\u003e \u003cp\u003e8.2 Rate Coefficients from Microscopic Dynamics 119\u003c\/p\u003e \u003cp\u003e8.2.1 Validity of First Order Kinetics 120\u003c\/p\u003e \u003cp\u003e8.2.2 Mapping Continuous Trajectories onto Discrete Kinetics and Computing Exact Rates 123\u003c\/p\u003e \u003cp\u003e8.2.3 Computing the Rate More Efficiently 126\u003c\/p\u003e \u003cp\u003e8.2.4 Transmission Coefficient and Variational Transition State Theory 128\u003c\/p\u003e \u003cp\u003e8.2.5 Harmonic Transition-State Theory 129\u003c\/p\u003e \u003cp\u003eReferences 131\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Zwanzig-Caldeiga-Leggett Model for Low-Dimensional Dynamics \u003c\/b\u003e\u003cb\u003e133\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Low-Dimensional Models of Reaction Dynamics From a Microscopic Hamiltonian 133\u003c\/p\u003e \u003cp\u003e9.2 Statistical Properties of the Noise and the Fluctuation-dissipation Theorem 137\u003c\/p\u003e \u003cp\u003e9.2.1 Ensemble Approach 138\u003c\/p\u003e \u003cp\u003e9.2.2 Single-Trajectory Approach 139\u003c\/p\u003e \u003cp\u003e9.3 Time-Reversibility of the Langevin Equation 142\u003c\/p\u003e \u003cp\u003eReferences 145\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Escape from a Potential Well in the Case of Dynamics Obeying the Generalized Langevin Equation: General Solution Based on the Zwanzig-Caldeira-Leggett Hamiltonian \u003c\/b\u003e\u003cb\u003e147\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Derivation of the Escape Rate 147\u003c\/p\u003e \u003cp\u003e10.2 The Limit of Kramers Theory 150\u003c\/p\u003e \u003cp\u003e10.3 Significance of Memory Effects 152\u003c\/p\u003e \u003cp\u003e10.4 Applications of the Kramers Theory to Chemical Kinetics in Condensed Phases, Particularly in Biomolecular Systems 153\u003c\/p\u003e \u003cp\u003e10.5 A Comment on the Use of the Term “Free Energy” in Application to Chemical Kinetics and Equilibrium 155\u003c\/p\u003e \u003cp\u003eReferences 156\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Diffusive Dynamics on a Multidimensional Energy Landscape \u003c\/b\u003e\u003cb\u003e157\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Generalized Langevin Equation with Exponential Memory can be Derived from a 2D Markov Model 157\u003c\/p\u003e \u003cp\u003e11.2 Theory of Multidimensional Barrier Crossing 161\u003c\/p\u003e \u003cp\u003e11.3 Breakdown of the Langer Theory in the Case of Anisotropic Diffusion: the Berezhkovskii-Zitserman Case 167\u003c\/p\u003e \u003cp\u003eReferences 171\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Quantum Effects in Chemical Kinetics \u003c\/b\u003e\u003cb\u003e173\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 When is a Quantum Mechanical Description Necessary? 173\u003c\/p\u003e \u003cp\u003e12.2 How Do the Laws of Quantum Mechanics Affect the Observed Transition Rates? 174\u003c\/p\u003e \u003cp\u003e12.3 Semiclassical Approximation and the Deep Tunneling Regime 177\u003c\/p\u003e \u003cp\u003e12.4 Path Integrals, Ring-Polymer Quantum Transition-State Theory, Instantons and Centroids 184\u003c\/p\u003e \u003cp\u003eReferences 191\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Computer Simulations of Molecular Kinetics: Foundation \u003c\/b\u003e\u003cb\u003e193\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Computer Simulations: Statement of Goals 193\u003c\/p\u003e \u003cp\u003e13.2 The Empirical Energy 195\u003c\/p\u003e \u003cp\u003e13.3 Molecular States 197\u003c\/p\u003e \u003cp\u003e13.4 Mean First Passage Time 199\u003c\/p\u003e \u003cp\u003e13.5 Coarse Variables 199\u003c\/p\u003e \u003cp\u003e13.6 Equilibrium, Stable, and Metastable States 200\u003c\/p\u003e \u003cp\u003eReferences 202\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 The Master Equation as a Model for Transitions Between Macrostates \u003c\/b\u003e\u003cb\u003e203\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReferences 211\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Direct Calculation of Rate Coefficients with Computer Simulations \u003c\/b\u003e\u003cb\u003e213\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e15.1 Computer Simulations of Trajectories 213\u003c\/p\u003e \u003cp\u003e15.2 Calculating Rate with Trajectories 219\u003c\/p\u003e \u003cp\u003eReferences 221\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 A Simple Numerical Example of Rate Calculations \u003c\/b\u003e\u003cb\u003e223\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReferences 231\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Rare Events and Reaction Coordinates \u003c\/b\u003e\u003cb\u003e233\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReferences 240\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 Celling \u003c\/b\u003e\u003cb\u003e241\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReferences 252\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 An Example of the Use of Cells: Alanine Dipeptide \u003c\/b\u003e\u003cb\u003e255\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReferences 257\u003c\/p\u003e \u003cp\u003eIndex 259\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eRon Elber\u003c\/b\u003e is Professor of Chemistry at the University of Texas at Austin and W. A. \"Tex\" Moncrief, Jr. Endowed Chair in Computational Life Sciences and Biology in the Oden Institute for Computational Engineering and Sciences. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eDmitrii E. Makarov\u003c\/b\u003e is Professor of Chemistry at the University of Texas at Austin. His research is in the field of computational and theoretical chemical physics. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eHenri Orland\u003c\/b\u003e is Directeur de Recherches at the Institut de Physique Théorique, the French Alternative Energies and Atomic Energy Commission, CEA, France.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eA guide to the theoretical and computational toolkits for the modern study of molecular kinetics in condensed phases\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eMolecular Kinetics in Condensed Phases: Theory, Simulation, and Analysis\u003c\/i\u003e puts the focus on the theory, algorithms, simulations methods and analysis of molecular kinetics in condensed phases. The authors – noted experts on the topic – offer a detailed and thorough description of modern theories and simulation methods to model molecular events. They highlight the rigorous stochastic modelling of molecular processes and the use of mathematical models to reproduce experimental observations, such as rate coefficients, mean first passage times and transition path times. \u003c\/p\u003e\u003cp\u003eThe book's exploration of simulations examines atomically detailed modelling of molecules in action and the connections of these simulations to theory and experiment. The authors also explore applications that range from simple intuitive examples of one- and two-dimensional systems to complex solvated macromolecules. This important book: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eOffers an introduction to the topic that combines theory, simulation and analysis\u003c\/li\u003e \u003cli\u003eIncludes a detailed study of a two-dimensional system and of a solvated peptide, as well as explanations and examples of how to conduct computer simulations of kinetics\u003c\/li\u003e \u003cli\u003eDiscusses modern developments in the field and explains their connection to the more traditional concepts in chemical dynamics\u003c\/li\u003e \u003cli\u003eIs written by well-known and highly-regarded leaders in the field\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eWritten for students and academic researchers in the fields of chemical kinetics, chemistry, computational statistical mechanics, biophysics and computational biology, \u003ci\u003eMolecular Kinetics in Condensed Phases\u003c\/i\u003e is the authoritative guide for the study of molecular kinetics in condensed phases.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989648359653,"sku":"NP9781119176770","price":102.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119176770.jpg?v=1761784951","url":"https:\/\/k12savings.com\/es\/products\/molecular-kinetics-in-condensed-phases-isbn-9781119176770","provider":"K12savings","version":"1.0","type":"link"}