{"product_id":"reviews-in-computational-chemistry-volume-31-isbn-9781119518020","title":"Reviews in Computational Chemistry, Volume 31","description":"The Reviews in Computational Chemistry series brings together leading authorities in the field to teach the newcomer and update the expert on topics centered on molecular modeling, such as computer-assisted molecular design (CAMD), quantum chemistry, molecular mechanics and dynamics, and quantitative structure-activity relationships (QSAR). This volume, like those prior to it, features chapters by experts in various fields of computational chemistry.  Topics in Volume 31 include:\u003cbr\u003e\u003cbr\u003eLattice-Boltzmann Modeling of Multicomponent Systems:  An Introduction\u003cbr\u003eModeling Mechanochemistry from First Principles\u003cbr\u003eMapping Energy Transport Networks in Proteins\u003cbr\u003eThe Role of Computations in Catalysis\u003cbr\u003eThe Construction of Ab Initio Based Potential Energy Surfaces\u003cbr\u003eUncertainty Quantification for Molecular Dynamics \u003cp\u003eList of Contributors ix\u003c\/p\u003e \u003cp\u003ePreface xi\u003c\/p\u003e \u003cp\u003eContributors to Previous Volumes xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Lattice-Boltzmann Modeling of Multicomponent Systems: An Introduction 1\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eUlf D. Schiller and Olga Kuksenok\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 1\u003c\/p\u003e \u003cp\u003eThe Lattice Boltzmann Equation: A Modern Introduction 4\u003c\/p\u003e \u003cp\u003eA Brief History of the LBM 5\u003c\/p\u003e \u003cp\u003eThe Lattice Boltzmann Equation 7\u003c\/p\u003e \u003cp\u003eThe Fluctuating Lattice Boltzmann Equation 23\u003c\/p\u003e \u003cp\u003eBoundary Conditions 25\u003c\/p\u003e \u003cp\u003eFluid–Particle Coupling 30\u003c\/p\u003e \u003cp\u003eLBM for Multiphase Fluids 37\u003c\/p\u003e \u003cp\u003eGoverning Continuum Equations 37\u003c\/p\u003e \u003cp\u003eLattice Boltzmann Algorithm for Binary Fluid: Free-Energy Approach 42\u003c\/p\u003e \u003cp\u003eMinimizing Spurious Velocities 47\u003c\/p\u003e \u003cp\u003eConclusions 50\u003c\/p\u003e \u003cp\u003eReferences 51\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Mapping Energy Transport Networks in Proteins 63\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDavid M. Leitner and Takahisa Yamato\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 63\u003c\/p\u003e \u003cp\u003eThermal and Energy Flow in Macromolecules 65\u003c\/p\u003e \u003cp\u003eNormal Modes of Proteins 65\u003c\/p\u003e \u003cp\u003eSimulating Energy Transport in Terms of Normal Modes 69\u003c\/p\u003e \u003cp\u003eEnergy Diffusion in Terms of Normal Modes 70\u003c\/p\u003e \u003cp\u003eEnergy Transport from Time Correlation Functions 73\u003c\/p\u003e \u003cp\u003eEnergy Transport in Proteins is Inherently Anisotropic 75\u003c\/p\u003e \u003cp\u003eLocating Energy Transport Networks 77\u003c\/p\u003e \u003cp\u003eCommunication Maps 77\u003c\/p\u003e \u003cp\u003eCURrent calculations for Proteins (CURP) 80\u003c\/p\u003e \u003cp\u003eApplications 83\u003c\/p\u003e \u003cp\u003eCommunication Maps: Illustrative Examples 83\u003c\/p\u003e \u003cp\u003eCURP: Illustrative Examples 89\u003c\/p\u003e \u003cp\u003eFuture Directions 98\u003c\/p\u003e \u003cp\u003eSummary 99\u003c\/p\u003e \u003cp\u003eAcknowledgments 100\u003c\/p\u003e \u003cp\u003eReferences 100\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Uncertainty Quantification for Molecular Dynamics 115\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePaul N. Patrone and Andrew Dienstfrey\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 115\u003c\/p\u003e \u003cp\u003eFrom Dynamical to Random: An Overview of MD 118\u003c\/p\u003e \u003cp\u003eSystem Specification 119\u003c\/p\u003e \u003cp\u003eInteratomic Potentials 121\u003c\/p\u003e \u003cp\u003eHamilton’s Equations 123\u003c\/p\u003e \u003cp\u003eThermodynamic Ensembles 128\u003c\/p\u003e \u003cp\u003eWhere Does This Leave Us? 131\u003c\/p\u003e \u003cp\u003eUncertainty Quantification 131\u003c\/p\u003e \u003cp\u003eWhat is UQ? 132\u003c\/p\u003e \u003cp\u003eTools for UQ 136\u003c\/p\u003e \u003cp\u003eUQ of MD 143\u003c\/p\u003e \u003cp\u003eTutorial: Trajectory Analysis 143\u003c\/p\u003e \u003cp\u003eTutorial: Ensemble Verification 148\u003c\/p\u003e \u003cp\u003eTutorial: UQ of Data Analysis for the Glass-Transition Temperature 151\u003c\/p\u003e \u003cp\u003eConcluding Thoughts 161\u003c\/p\u003e \u003cp\u003eReferences 162\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 The Role of Computations in Catalysis 171\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eHoria Metiu, Vishal Agarwal, and Henrik H. Kristoffersen\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 171\u003c\/p\u003e \u003cp\u003eScreening 172\u003c\/p\u003e \u003cp\u003eSabatier Principle 173\u003c\/p\u003e \u003cp\u003eScaling Relations 175\u003c\/p\u003e \u003cp\u003eBEP Relationship 176\u003c\/p\u003e \u003cp\u003eVolcano Plots 180\u003c\/p\u003e \u003cp\u003eSome Rules for Oxide Catalysts 189\u003c\/p\u003e \u003cp\u003eLet Us Examine Some Industrial Catalysts 191\u003c\/p\u003e \u003cp\u003eSometimes Selectivity is More Important than Rate 191\u003c\/p\u003e \u003cp\u003eSometimes We Want a Smaller Rate! 191\u003c\/p\u003e \u003cp\u003eSometimes Product Separation is More Important than the Reaction Rate 193\u003c\/p\u003e \u003cp\u003eSome Reactions are Equilibrium-limited 193\u003c\/p\u003e \u003cp\u003eThe Cost of Making the Catalyst is Important 194\u003c\/p\u003e \u003cp\u003eThe Catalyst Should Contain Abundant Elements 194\u003c\/p\u003e \u003cp\u003eA Good Catalyst Should not be Easily Poisoned 195\u003c\/p\u003e \u003cp\u003eSummary 195\u003c\/p\u003e \u003cp\u003eReferences 196\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 The Construction of Ab Initio-Based Potential Energy Surfaces 199\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eRichard Dawes and Ernesto Quintas-Sánchez\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction and Overview 199\u003c\/p\u003e \u003cp\u003eWhat is a PES? 199\u003c\/p\u003e \u003cp\u003eSignificance and Range of Applications of PESs 204\u003c\/p\u003e \u003cp\u003eChallenges for Theory 207\u003c\/p\u003e \u003cp\u003eTerminology and Concepts 209\u003c\/p\u003e \u003cp\u003eThe Schrödinger Equation 209\u003c\/p\u003e \u003cp\u003eThe BO Approximation 210\u003c\/p\u003e \u003cp\u003eMathematical Foundations of (Linear) Fitting 215\u003c\/p\u003e \u003cp\u003eQuantum Chemistry Methods 221\u003c\/p\u003e \u003cp\u003eGeneral Considerations 221\u003c\/p\u003e \u003cp\u003eSingle Reference Methods 223\u003c\/p\u003e \u003cp\u003eMultireference Methods 225\u003c\/p\u003e \u003cp\u003eCompound Methods or Protocols 227\u003c\/p\u003e \u003cp\u003eFitting Methods 229\u003c\/p\u003e \u003cp\u003eGeneral Considerations and Desirable Attributes of a PES 229\u003c\/p\u003e \u003cp\u003eNon-Interpolative Fitting Methods 231\u003c\/p\u003e \u003cp\u003eInterpolative Fitting Methods 239\u003c\/p\u003e \u003cp\u003eApplications 242\u003c\/p\u003e \u003cp\u003eThe Automated Construction of PESs 242\u003c\/p\u003e \u003cp\u003eConcluding Remarks 248\u003c\/p\u003e \u003cp\u003eAcknowledgements 250\u003c\/p\u003e \u003cp\u003eAcronyms\/Abbreviations 250\u003c\/p\u003e \u003cp\u003eReferences 251\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Modeling Mechanochemistry from First Principles 265\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eHeather J. Kulik\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction and Scope 265\u003c\/p\u003e \u003cp\u003ePotential Energy Surfaces and Reaction Coordinates 266\u003c\/p\u003e \u003cp\u003eTheoretical Models of Mechanochemical Bond Cleavage 268\u003c\/p\u003e \u003cp\u003eLinear Model (Kauzmann, Eyring, and Bell) 268\u003c\/p\u003e \u003cp\u003eTilted Potential Energy Profile Model 270\u003c\/p\u003e \u003cp\u003eFirst-Principles Models for Mechanochemical Bond Cleavage 271\u003c\/p\u003e \u003cp\u003eConstrained Geometries Simulate External Force (COGEF) 271\u003c\/p\u003e \u003cp\u003eForce-Modified Potential Energy Surfaces 273\u003c\/p\u003e \u003cp\u003eCovalent Mechanochemistry 278\u003c\/p\u003e \u003cp\u003eOverview of Characterization Methods 278\u003c\/p\u003e \u003cp\u003eRepresentative Mechanophores 280\u003c\/p\u003e \u003cp\u003eRepresentative Mechanochemistry Case Studies 281\u003c\/p\u003e \u003cp\u003eBenzocyclobutene 281\u003c\/p\u003e \u003cp\u003egem-Difluorocyclopropane 285\u003c\/p\u003e \u003cp\u003ePPA: Heterolytic Bond Cleavage 288\u003c\/p\u003e \u003cp\u003eMechanical Force for Sampling: Application to Lignin 292\u003c\/p\u003e \u003cp\u003eBest Practices for Mechanochemical Simulation 296\u003c\/p\u003e \u003cp\u003eConclusions 298\u003c\/p\u003e \u003cp\u003eAcknowledgments 299\u003c\/p\u003e \u003cp\u003eReferences 300\u003c\/p\u003e \u003cp\u003eIndex 313\u003cb\u003e \u003c\/b\u003e\u003c\/p\u003e \t \u003cp\u003e\u003cb\u003eABBY L. PARRILL, Ph.D.,\u003c\/b\u003e is a Professor in the Department of Chemistry at the University of Memphis, TN. Her research interests are in bioorganic chemistry, protein modeling, and rational ligand design and synthesis. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eKENNY B. LIPKOWITZ, Ph.D.,\u003c\/b\u003e was one of the founding co-editors of Reviews in Computational Chemistry. He spent 28 years as an academician and then moved to the Office of Naval Research where he is a Program Manager in Computer-Aided Materials Design.  \t \u003c\/p\u003e\u003cp\u003e\u003cb\u003eA VALUABLE REFERENCE TO THE METHODS AND TECHNIQUES IN COMPUTATIONAL CHEMISTRY\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eReviews in Computational Chemistry, Volume 31\u003c\/i\u003e brings together in one book a collection of writings from noted authorities in the field. Volume 31 is designed for use by both those new to the field and for seasoned researchers to aid them in selecting and applying new computational chemistry methods to their own research problems. The book's tutorial-style chapters provide both mini-tutorials for novices as well as critical literature reviews highlighting advanced applications. \u003c\/p\u003e\u003cp\u003eTwo themes connect many of the chapters: modeling of soft matter systems such as polymers and proteins and the first principle methods necessary for modeling chemical reactions. The contributors cover a wealth of topics centered on molecular modeling, such as modeling mechanochemical processes and protein internal energy transfer networks, lattice Boltzmann simulations, ab initio potential energy surface construction, catalyst optimization, and uncertainty quantification. This important resource: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eOffers a guide to the both the background and theory and the strategies for using methods correctly\u003c\/li\u003e \u003cli\u003eIncludes information on the pitfalls to avoid, applications, and references\u003c\/li\u003e \u003cli\u003ePresents a detailed subject index to help quickly discover particular topics\u003c\/li\u003e \u003cli\u003eUses a tutorial manner and non-mathematical style, that helps to access computational methods outside one's immediate area of expertise\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eWritten for computational chemists, theoretical chemists, pharmaceutical chemists, biological chemists, chemical engineers, and others. \u003ci\u003eReviews in Computational Chemistry, Volume 31\u003c\/i\u003e is an essential guide to the modeling of soft manner systems and explains the principle methods needed for modeling chemical reactions.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989960540389,"sku":"NP9781119518020","price":318.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119518020.jpg?v=1761786036","url":"https:\/\/k12savings.com\/es\/products\/reviews-in-computational-chemistry-volume-31-isbn-9781119518020","provider":"K12savings","version":"1.0","type":"link"}