{"product_id":"reviews-in-computational-chemistry-volume-29-isbn-9781119103936","title":"Reviews in Computational Chemistry, Volume 29","description":"\u003cp\u003eThe \u003cb\u003eReviews in Computational Chemistry\u003c\/b\u003e 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 29 include:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eNoncovalent Interactions in Density-Functional Theory\u003c\/li\u003e \u003cli\u003eLong-Range Inter-Particle Interactions:  Insights from Molecular Quantum Electrodynamics (QED) Theory\u003c\/li\u003e \u003cli\u003eEfficient Transition-State Modeling using Molecular Mechanics Force Fields for the Everyday Chemist\u003c\/li\u003e \u003cli\u003eMachine Learning in Materials Science:  Recent Progress and Emerging Applications\u003c\/li\u003e \u003cli\u003eDiscovering New Materials via \u003ci\u003ea priori \u003c\/i\u003eCrystal Structure Prediction\u003c\/li\u003e \u003cli\u003eIntroduction to Maximally Localized Wannier Functions\u003c\/li\u003e \u003cli\u003eMethods for a Rapid and Automated Description of Proteins: Protein Structure, Protein Similarity, and Protein Folding\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eContributors x\u003c\/p\u003e \u003cp\u003ePreface xii\u003c\/p\u003e \u003cp\u003eContributors to Previous Volumes xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Noncovalent Interactions in Density Functional Theory 1\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eGino A. DiLabio and Alberto Otero-de-la-Roza\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 1\u003c\/p\u003e \u003cp\u003eOverview of Noncovalent Interactions 3\u003c\/p\u003e \u003cp\u003eTheory Background 9\u003c\/p\u003e \u003cp\u003eDensity-Functional Theory 9\u003c\/p\u003e \u003cp\u003eFailure of Conventional DFT for Noncovalent Interactions 17\u003c\/p\u003e \u003cp\u003eNoncovalent Interactions in DFT 20\u003c\/p\u003e \u003cp\u003ePairwise Dispersion Corrections 20\u003c\/p\u003e \u003cp\u003ePotential-Based Methods 42\u003c\/p\u003e \u003cp\u003eMinnesota Functionals 47\u003c\/p\u003e \u003cp\u003eNonlocal Functionals 54\u003c\/p\u003e \u003cp\u003ePerformance of Density Functionals for Noncovalent Interactions 59\u003c\/p\u003e \u003cp\u003eDescription of Noncovalent Interactions Benchmarks 59\u003c\/p\u003e \u003cp\u003ePerformance of Dispersion-Corrected Methods 66\u003c\/p\u003e \u003cp\u003eNoncovalent Interactions in Perspective 74\u003c\/p\u003e \u003cp\u003eAcknowledgments 78\u003c\/p\u003e \u003cp\u003eReferences 79\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Long-Range Interparticle Interactions: Insights from Molecular Quantum Electrodynamics (QED) Theory 98\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eAkbar Salam\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 98\u003c\/p\u003e \u003cp\u003eThe Interaction Energy at Long Range 101\u003c\/p\u003e \u003cp\u003eMolecular QED Theory 104\u003c\/p\u003e \u003cp\u003eElectrostatic Interaction in Multipolar QED 112\u003c\/p\u003e \u003cp\u003eEnergy Transfer 114\u003c\/p\u003e \u003cp\u003eMediation of RET by a Third Body 119\u003c\/p\u003e \u003cp\u003eDispersion Potential between a Pair of Atoms or Molecules 123\u003c\/p\u003e \u003cp\u003eTriple–Dipole Dispersion Potential 128\u003c\/p\u003e \u003cp\u003eDispersion Force Induced by External Radiation 132\u003c\/p\u003e \u003cp\u003eMacroscopic QED 136\u003c\/p\u003e \u003cp\u003eSummary 141\u003c\/p\u003e \u003cp\u003eReferences 143\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Efficient Transition State Modeling Using Molecular Mechanics Force Fields for the Everyday Chemist 152\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eJoshua Pottel and Nicolas Moitessier\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 152\u003c\/p\u003e \u003cp\u003eMolecular Mechanics and Transition State Basics 154\u003c\/p\u003e \u003cp\u003eMolecular Mechanics 154\u003c\/p\u003e \u003cp\u003eTransition States 157\u003c\/p\u003e \u003cp\u003eGround State Force Field Techniques 158\u003c\/p\u003e \u003cp\u003eIntroduction 158\u003c\/p\u003e \u003cp\u003eReaxFF 159\u003c\/p\u003e \u003cp\u003eReaction Force Field 161\u003c\/p\u003e \u003cp\u003eSeam 163\u003c\/p\u003e \u003cp\u003eEmpirical Valence Bond\/Multiconfiguration Molecular Dynamics 166\u003c\/p\u003e \u003cp\u003eAsymmetric Catalyst Evaluation 169\u003c\/p\u003e \u003cp\u003eTSFF Techniques 173\u003c\/p\u003e \u003cp\u003eIntroduction 173\u003c\/p\u003e \u003cp\u003eQ2MM 175\u003c\/p\u003e \u003cp\u003eConclusion and Prospects 178\u003c\/p\u003e \u003cp\u003eReferences 178\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Machine Learning in Materials Science: Recent Progress and Emerging Applications 186\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eTim Mueller, Aaron Gilad Kusne, and Rampi Ramprasad\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 186\u003c\/p\u003e \u003cp\u003eSupervised Learning 188\u003c\/p\u003e \u003cp\u003eA Formal Probabilistic Basis for Supervised Learning 189\u003c\/p\u003e \u003cp\u003eSupervised Learning Algorithms 199\u003c\/p\u003e \u003cp\u003eUnsupervised Learning 213\u003c\/p\u003e \u003cp\u003eCluster Analysis 215\u003c\/p\u003e \u003cp\u003eDimensionality Reduction 226\u003c\/p\u003e \u003cp\u003eSelected Materials Science Applications 237\u003c\/p\u003e \u003cp\u003ePhase Diagram Determination 237\u003c\/p\u003e \u003cp\u003eMaterials Property Predictions Based on Data from Quantum Mechanical Computations 240\u003c\/p\u003e \u003cp\u003eDevelopment of Interatomic Potentials 245\u003c\/p\u003e \u003cp\u003eCrystal Structure Predictions (CSPs) 249\u003c\/p\u003e \u003cp\u003eDeveloping and Discovering Density Functionals 250\u003c\/p\u003e \u003cp\u003eLattice Models 251\u003c\/p\u003e \u003cp\u003eMaterials Processing and Complex Materials Behavior 256\u003c\/p\u003e \u003cp\u003eAutomated Micrograph Analysis 257\u003c\/p\u003e \u003cp\u003eStructure–Property Relationships in Amorphous Materials 260\u003c\/p\u003e \u003cp\u003eAdditional Resources 263\u003c\/p\u003e \u003cp\u003eSummary 263\u003c\/p\u003e \u003cp\u003eAcknowledgments 264\u003c\/p\u003e \u003cp\u003eReferences 264\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Discovering New Materials via A Priori Crystal Structure Prediction 274\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eEva Zurek\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction and Scope 274\u003c\/p\u003e \u003cp\u003eCrystal Lattices and Potential Energy Surfaces 276\u003c\/p\u003e \u003cp\u003eCalculating Energies and Optimizing Geometries 281\u003c\/p\u003e \u003cp\u003eMethods to Predict Crystal Structures 282\u003c\/p\u003e \u003cp\u003eFollowing Soft Vibrational Modes 283\u003c\/p\u003e \u003cp\u003eRandom (Sensible) Structure Searches 284\u003c\/p\u003e \u003cp\u003eSimulated Annealing 285\u003c\/p\u003e \u003cp\u003eBasin Hopping and Minima Hopping 287\u003c\/p\u003e \u003cp\u003eMetadynamics 288\u003c\/p\u003e \u003cp\u003eParticle Swarm Optimization 289\u003c\/p\u003e \u003cp\u003eGenetic Algorithms and Evolutionary Algorithms 291\u003c\/p\u003e \u003cp\u003eHybrid Methods 292\u003c\/p\u003e \u003cp\u003eThe Nitty-Gritty Aspects of Evolutionary Algorithms 294\u003c\/p\u003e \u003cp\u003eWorkflow 294\u003c\/p\u003e \u003cp\u003eSelection for Procreation 295\u003c\/p\u003e \u003cp\u003eEvolutionary Operators 297\u003c\/p\u003e \u003cp\u003eMaintaining Diversity 299\u003c\/p\u003e \u003cp\u003eThe XtalOpt Evolutionary Algorithm 300\u003c\/p\u003e \u003cp\u003ePractical Aspects of Carrying out an Evolutionary Structure Search 303\u003c\/p\u003e \u003cp\u003eCrystal Structure Prediction at Extreme Pressures 312\u003c\/p\u003e \u003cp\u003eNote in Proof 315\u003c\/p\u003e \u003cp\u003eConclusions 316\u003c\/p\u003e \u003cp\u003eAcknowledgments 317\u003c\/p\u003e \u003cp\u003eReferences 317\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Introduction to Maximally Localized Wannier Functions 327\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eAlberto Ambrosetti and Pier Luigi Silvestrelli\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 327\u003c\/p\u003e \u003cp\u003eTheory 329\u003c\/p\u003e \u003cp\u003eBloch States 329\u003c\/p\u003e \u003cp\u003eWannier Functions 331\u003c\/p\u003e \u003cp\u003eMaximally Localized Wannier Functions: Γ-Point Formulation 333\u003c\/p\u003e \u003cp\u003eExtension to Brillouin-Zone k]Point Sampling 336\u003c\/p\u003e \u003cp\u003eDegree of WF Localization 337\u003c\/p\u003e \u003cp\u003eEntangled Bands and Subspace Selection 338\u003c\/p\u003e \u003cp\u003eApplications 340\u003c\/p\u003e \u003cp\u003eCharge Visualization 340\u003c\/p\u003e \u003cp\u003eCharge Polarization 344\u003c\/p\u003e \u003cp\u003eBonding Analysis 348\u003c\/p\u003e \u003cp\u003eAmorphous Phases and Defects 351\u003c\/p\u003e \u003cp\u003eElectron Transport 354\u003c\/p\u003e \u003cp\u003eEfficient Basis Sets 356\u003c\/p\u003e \u003cp\u003eHints About MLWFs Numerical Computation 361\u003c\/p\u003e \u003cp\u003eBrief Review of the Presently Available Computational Tools 361\u003c\/p\u003e \u003cp\u003eMLWF Generation 362\u003c\/p\u003e \u003cp\u003eReferences 363\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Methods for a Rapid and Automated Description of Proteins: Protein Structure, Protein Similarity, and Protein Folding 369\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eZhanyong Guo and Dieter Cremer\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eIntroduction 369\u003c\/p\u003e \u003cp\u003eProtein Structure Description Methods Based on Frenet Coordinates and\/or Coarse Graining 373\u003c\/p\u003e \u003cp\u003eThe Automated Protein Structure Analysis (APSA) 375\u003c\/p\u003e \u003cp\u003eThe Curvature–Torsion Description for Idealized Secondary Structures 378\u003c\/p\u003e \u003cp\u003eIdentification of Helices, Strands, and Coils 384\u003c\/p\u003e \u003cp\u003eDifference between Geometry-Based and H]Bond-Based Methods 385\u003c\/p\u003e \u003cp\u003eCombination of Geometry-Based and H-Bond]Based Methods 388\u003c\/p\u003e \u003cp\u003eChirality of SSUs 388\u003c\/p\u003e \u003cp\u003eWhat is a Regular SSU? 389\u003c\/p\u003e \u003cp\u003eA Closer Look at Helices: Distinction between α- and 310-Helices 391\u003c\/p\u003e \u003cp\u003eTypical Helix Distortions 395\u003c\/p\u003e \u003cp\u003eLevel 2 of Coarse Graining: The Curved Vector Presentation of Helices 398\u003c\/p\u003e \u003cp\u003eIdentification of Kinked Helices 402\u003c\/p\u003e \u003cp\u003eAnalysis of Turns 406\u003c\/p\u003e \u003cp\u003eIntroduction of a Structural Alphabet 409\u003c\/p\u003e \u003cp\u003eDerivation of a Protein Structure Code 411\u003c\/p\u003e \u003cp\u003eDescription of Protein Similarity 416\u003c\/p\u003e \u003cp\u003eQualitative and Quantitative Assessment of Protein Similarity 417\u003c\/p\u003e \u003cp\u003eThe Secondary Code and Its Application in Connection with Protein Similarity 423\u003c\/p\u003e \u003cp\u003eDescription of Protein Folding 423\u003c\/p\u003e \u003cp\u003eConcluding Remarks 426\u003c\/p\u003e \u003cp\u003eAcknowledgments 428\u003c\/p\u003e \u003cp\u003eReferences 428\u003c\/p\u003e \u003cp\u003eIndex 439\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAbby L. Parrill,\u003c\/b\u003e PhD, is Professor of Chemistry in the Department of Chemistry at the University of Memphis, TN. Her research interests are in bioorganic chemistry, protein modeling and NMR Spectroscopy and rational ligand design and synthesis. In 2011, she was awarded the Distinguished Research Award by University of Memphis Alumni Association. She has given more than 100 presentations,  more than 100 papers and books.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eKenny B. Lipkowitz\u003c\/b\u003e, PhD, is a recently retired Professor of Chemistry from North Dakota State University.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989960507621,"sku":"NP9781119103936","price":218.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119103936.jpg?v=1761786036","url":"https:\/\/k12savings.com\/es\/products\/reviews-in-computational-chemistry-volume-29-isbn-9781119103936","provider":"K12savings","version":"1.0","type":"link"}