Abby L. Parrill, 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.Kenny B. Lipkowitz, PhD, is a recently retired Professor of Chemistry from North Dakota State University.

• Contributors xPreface xiiContributors to Previous Volumes xv1 Noncovalent Interactions in Density Functional Theory 1Gino A. DiLabio and Alberto Otero-de-la-RozaIntroduction 1Overview of Noncovalent Interactions 3Theory Background 9Density?-Functional Theory 9Failure of Conventional DFT for Noncovalent Interactions 17Noncovalent Interactions in DFT 20Pairwise Dispersion Corrections 20Potential-Based Methods 42Minnesota Functionals 47Nonlocal Functionals 54Performance of Density Functionals for Noncovalent Interactions 59Description of Noncovalent Interactions Benchmarks 59Performance of Dispersion-Corrected Methods 66Noncovalent Interactions in Perspective 74Acknowledgments 78References 792 Long?-Range Interparticle Interactions: Insights from Molecular Quantum Electrodynamics (QED) Theory 98Akbar SalamIntroduction 98The Interaction Energy at Long Range 101Molecular QED Theory 104Electrostatic Interaction in Multipolar QED 112Energy Transfer 114Mediation of RET by a Third Body 119Dispersion Potential between a Pair of Atoms or Molecules 123Triple–Dipole Dispersion Potential 128Dispersion Force Induced by External Radiation 132Macroscopic QED 136Summary 141References 1433 Efficient Transition State Modeling Using Molecular Mechanics Force Fields for the Everyday Chemist 152Joshua Pottel and Nicolas MoitessierIntroduction 152Molecular Mechanics and Transition State Basics 154Molecular Mechanics 154Transition States 157Ground State Force Field Techniques 158Introduction 158ReaxFF 159Reaction Force Field 161Seam 163Empirical Valence Bond/Multiconfiguration Molecular Dynamics 166Asymmetric Catalyst Evaluation 169TSFF Techniques 173Introduction 173Q2MM 175Conclusion and Prospects 178References 1784 Machine Learning in Materials Science: Recent Progress and Emerging Applications 186Tim Mueller, Aaron Gilad Kusne, and Rampi RamprasadIntroduction 186Supervised Learning 188A Formal Probabilistic Basis for Supervised Learning 189Supervised Learning Algorithms 199Unsupervised Learning 213Cluster Analysis 215Dimensionality Reduction 226Selected Materials Science Applications 237Phase Diagram Determination 237Materials Property Predictions Based on Data from Quantum Mechanical Computations 240Development of Interatomic Potentials 245Crystal Structure Predictions (CSPs) 249Developing and Discovering Density Functionals 250Lattice Models 251Materials Processing and Complex Materials Behavior 256Automated Micrograph Analysis 257Structure–Property Relationships in Amorphous Materials 260Additional Resources 263Summary 263Acknowledgments 264References 2645 Discovering New Materials via A Priori Crystal Structure Prediction 274Eva ZurekIntroduction and Scope 274Crystal Lattices and Potential Energy Surfaces 276Calculating Energies and Optimizing Geometries 281Methods to Predict Crystal Structures 282Following Soft Vibrational Modes 283Random (Sensible) Structure Searches 284Simulated Annealing 285Basin Hopping and Minima Hopping 287Metadynamics 288Particle Swarm Optimization 289Genetic Algorithms and Evolutionary Algorithms 291Hybrid Methods 292The Nitty?-Gritty Aspects of Evolutionary Algorithms 294Workflow 294Selection for Procreation 295Evolutionary Operators 297Maintaining Diversity 299The XtalOpt Evolutionary Algorithm 300Practical Aspects of Carrying out an Evolutionary Structure Search 303Crystal Structure Prediction at Extreme Pressures 312Note in Proof 315Conclusions 316Acknowledgments 317References 3176 Introduction to Maximally Localized Wannier Functions 327Alberto Ambrosetti and Pier Luigi SilvestrelliIntroduction 327Theory 329Bloch States 329Wannier Functions 331Maximally Localized Wannier Functions: Γ-Point Formulation 333Extension to Brillouin-Zone k?]Point Sampling 336Degree of WF Localization 337Entangled Bands and Subspace Selection 338Applications 340Charge Visualization 340Charge Polarization 344Bonding Analysis 348Amorphous Phases and Defects 351Electron Transport 354Efficient Basis Sets 356Hints About MLWFs Numerical Computation 361Brief Review of the Presently Available Computational Tools 361MLWF Generation 362References 3637 Methods for a Rapid and Automated Description of Proteins: Protein Structure, Protein Similarity, and Protein Folding 369Zhanyong Guo and Dieter CremerIntroduction 369Protein Structure Description Methods Based on Frenet Coordinates and/or Coarse Graining 373The Automated Protein Structure Analysis (APSA) 375The Curvature–Torsion Description for Idealized Secondary Structures 378Identification of Helices, Strands, and Coils 384Difference between Geometry?-Based and H?]Bond?-Based Methods 385Combination of Geometry?-Based and H?-Bond?]Based Methods 388Chirality of SSUs 388What is a Regular SSU? 389A Closer Look at Helices: Distinction between α- and 310?-Helices 391Typical Helix Distortions 395Level 2 of Coarse Graining: The Curved Vector Presentation of Helices 398Identification of Kinked Helices 402Analysis of Turns 406Introduction of a Structural Alphabet 409Derivation of a Protein Structure Code 411Description of Protein Similarity 416Qualitative and Quantitative Assessment of Protein Similarity 417The Secondary Code and Its Application in Connection with Protein Similarity 423Description of Protein Folding 423Concluding Remarks 426Acknowledgments 428References 428Index 439

Abby L. Parrill, 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.Kenny B. Lipkowitz, PhD, is a recently retired Professor of Chemistry from North Dakota State University.

• Contributors xPreface xiiContributors to Previous Volumes xv1 Noncovalent Interactions in Density Functional Theory 1Gino A. DiLabio and Alberto Otero-de-la-RozaIntroduction 1Overview of Noncovalent Interactions 3Theory Background 9Density?-Functional Theory 9Failure of Conventional DFT for Noncovalent Interactions 17Noncovalent Interactions in DFT 20Pairwise Dispersion Corrections 20Potential-Based Methods 42Minnesota Functionals 47Nonlocal Functionals 54Performance of Density Functionals for Noncovalent Interactions 59Description of Noncovalent Interactions Benchmarks 59Performance of Dispersion-Corrected Methods 66Noncovalent Interactions in Perspective 74Acknowledgments 78References 792 Long?-Range Interparticle Interactions: Insights from Molecular Quantum Electrodynamics (QED) Theory 98Akbar SalamIntroduction 98The Interaction Energy at Long Range 101Molecular QED Theory 104Electrostatic Interaction in Multipolar QED 112Energy Transfer 114Mediation of RET by a Third Body 119Dispersion Potential between a Pair of Atoms or Molecules 123Triple–Dipole Dispersion Potential 128Dispersion Force Induced by External Radiation 132Macroscopic QED 136Summary 141References 1433 Efficient Transition State Modeling Using Molecular Mechanics Force Fields for the Everyday Chemist 152Joshua Pottel and Nicolas MoitessierIntroduction 152Molecular Mechanics and Transition State Basics 154Molecular Mechanics 154Transition States 157Ground State Force Field Techniques 158Introduction 158ReaxFF 159Reaction Force Field 161Seam 163Empirical Valence Bond/Multiconfiguration Molecular Dynamics 166Asymmetric Catalyst Evaluation 169TSFF Techniques 173Introduction 173Q2MM 175Conclusion and Prospects 178References 1784 Machine Learning in Materials Science: Recent Progress and Emerging Applications 186Tim Mueller, Aaron Gilad Kusne, and Rampi RamprasadIntroduction 186Supervised Learning 188A Formal Probabilistic Basis for Supervised Learning 189Supervised Learning Algorithms 199Unsupervised Learning 213Cluster Analysis 215Dimensionality Reduction 226Selected Materials Science Applications 237Phase Diagram Determination 237Materials Property Predictions Based on Data from Quantum Mechanical Computations 240Development of Interatomic Potentials 245Crystal Structure Predictions (CSPs) 249Developing and Discovering Density Functionals 250Lattice Models 251Materials Processing and Complex Materials Behavior 256Automated Micrograph Analysis 257Structure–Property Relationships in Amorphous Materials 260Additional Resources 263Summary 263Acknowledgments 264References 2645 Discovering New Materials via A Priori Crystal Structure Prediction 274Eva ZurekIntroduction and Scope 274Crystal Lattices and Potential Energy Surfaces 276Calculating Energies and Optimizing Geometries 281Methods to Predict Crystal Structures 282Following Soft Vibrational Modes 283Random (Sensible) Structure Searches 284Simulated Annealing 285Basin Hopping and Minima Hopping 287Metadynamics 288Particle Swarm Optimization 289Genetic Algorithms and Evolutionary Algorithms 291Hybrid Methods 292The Nitty?-Gritty Aspects of Evolutionary Algorithms 294Workflow 294Selection for Procreation 295Evolutionary Operators 297Maintaining Diversity 299The XtalOpt Evolutionary Algorithm 300Practical Aspects of Carrying out an Evolutionary Structure Search 303Crystal Structure Prediction at Extreme Pressures 312Note in Proof 315Conclusions 316Acknowledgments 317References 3176 Introduction to Maximally Localized Wannier Functions 327Alberto Ambrosetti and Pier Luigi SilvestrelliIntroduction 327Theory 329Bloch States 329Wannier Functions 331Maximally Localized Wannier Functions: Γ-Point Formulation 333Extension to Brillouin-Zone k?]Point Sampling 336Degree of WF Localization 337Entangled Bands and Subspace Selection 338Applications 340Charge Visualization 340Charge Polarization 344Bonding Analysis 348Amorphous Phases and Defects 351Electron Transport 354Efficient Basis Sets 356Hints About MLWFs Numerical Computation 361Brief Review of the Presently Available Computational Tools 361MLWF Generation 362References 3637 Methods for a Rapid and Automated Description of Proteins: Protein Structure, Protein Similarity, and Protein Folding 369Zhanyong Guo and Dieter CremerIntroduction 369Protein Structure Description Methods Based on Frenet Coordinates and/or Coarse Graining 373The Automated Protein Structure Analysis (APSA) 375The Curvature–Torsion Description for Idealized Secondary Structures 378Identification of Helices, Strands, and Coils 384Difference between Geometry?-Based and H?]Bond?-Based Methods 385Combination of Geometry?-Based and H?-Bond?]Based Methods 388Chirality of SSUs 388What is a Regular SSU? 389A Closer Look at Helices: Distinction between α- and 310?-Helices 391Typical Helix Distortions 395Level 2 of Coarse Graining: The Curved Vector Presentation of Helices 398Identification of Kinked Helices 402Analysis of Turns 406Introduction of a Structural Alphabet 409Derivation of a Protein Structure Code 411Description of Protein Similarity 416Qualitative and Quantitative Assessment of Protein Similarity 417The Secondary Code and Its Application in Connection with Protein Similarity 423Description of Protein Folding 423Concluding Remarks 426Acknowledgments 428References 428Index 439

Abby L. Parrill, 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.Kenny B. Lipkowitz, PhD, is a recently retired Professor of Chemistry from North Dakota State University.

• Contributors xPreface xiiContributors to Previous Volumes xv1 Noncovalent Interactions in Density Functional Theory 1Gino A. DiLabio and Alberto Otero-de-la-RozaIntroduction 1Overview of Noncovalent Interactions 3Theory Background 9Density?-Functional Theory 9Failure of Conventional DFT for Noncovalent Interactions 17Noncovalent Interactions in DFT 20Pairwise Dispersion Corrections 20Potential-Based Methods 42Minnesota Functionals 47Nonlocal Functionals 54Performance of Density Functionals for Noncovalent Interactions 59Description of Noncovalent Interactions Benchmarks 59Performance of Dispersion-Corrected Methods 66Noncovalent Interactions in Perspective 74Acknowledgments 78References 792 Long?-Range Interparticle Interactions: Insights from Molecular Quantum Electrodynamics (QED) Theory 98Akbar SalamIntroduction 98The Interaction Energy at Long Range 101Molecular QED Theory 104Electrostatic Interaction in Multipolar QED 112Energy Transfer 114Mediation of RET by a Third Body 119Dispersion Potential between a Pair of Atoms or Molecules 123Triple–Dipole Dispersion Potential 128Dispersion Force Induced by External Radiation 132Macroscopic QED 136Summary 141References 1433 Efficient Transition State Modeling Using Molecular Mechanics Force Fields for the Everyday Chemist 152Joshua Pottel and Nicolas MoitessierIntroduction 152Molecular Mechanics and Transition State Basics 154Molecular Mechanics 154Transition States 157Ground State Force Field Techniques 158Introduction 158ReaxFF 159Reaction Force Field 161Seam 163Empirical Valence Bond/Multiconfiguration Molecular Dynamics 166Asymmetric Catalyst Evaluation 169TSFF Techniques 173Introduction 173Q2MM 175Conclusion and Prospects 178References 1784 Machine Learning in Materials Science: Recent Progress and Emerging Applications 186Tim Mueller, Aaron Gilad Kusne, and Rampi RamprasadIntroduction 186Supervised Learning 188A Formal Probabilistic Basis for Supervised Learning 189Supervised Learning Algorithms 199Unsupervised Learning 213Cluster Analysis 215Dimensionality Reduction 226Selected Materials Science Applications 237Phase Diagram Determination 237Materials Property Predictions Based on Data from Quantum Mechanical Computations 240Development of Interatomic Potentials 245Crystal Structure Predictions (CSPs) 249Developing and Discovering Density Functionals 250Lattice Models 251Materials Processing and Complex Materials Behavior 256Automated Micrograph Analysis 257Structure–Property Relationships in Amorphous Materials 260Additional Resources 263Summary 263Acknowledgments 264References 2645 Discovering New Materials via A Priori Crystal Structure Prediction 274Eva ZurekIntroduction and Scope 274Crystal Lattices and Potential Energy Surfaces 276Calculating Energies and Optimizing Geometries 281Methods to Predict Crystal Structures 282Following Soft Vibrational Modes 283Random (Sensible) Structure Searches 284Simulated Annealing 285Basin Hopping and Minima Hopping 287Metadynamics 288Particle Swarm Optimization 289Genetic Algorithms and Evolutionary Algorithms 291Hybrid Methods 292The Nitty?-Gritty Aspects of Evolutionary Algorithms 294Workflow 294Selection for Procreation 295Evolutionary Operators 297Maintaining Diversity 299The XtalOpt Evolutionary Algorithm 300Practical Aspects of Carrying out an Evolutionary Structure Search 303Crystal Structure Prediction at Extreme Pressures 312Note in Proof 315Conclusions 316Acknowledgments 317References 3176 Introduction to Maximally Localized Wannier Functions 327Alberto Ambrosetti and Pier Luigi SilvestrelliIntroduction 327Theory 329Bloch States 329Wannier Functions 331Maximally Localized Wannier Functions: Γ-Point Formulation 333Extension to Brillouin-Zone k?]Point Sampling 336Degree of WF Localization 337Entangled Bands and Subspace Selection 338Applications 340Charge Visualization 340Charge Polarization 344Bonding Analysis 348Amorphous Phases and Defects 351Electron Transport 354Efficient Basis Sets 356Hints About MLWFs Numerical Computation 361Brief Review of the Presently Available Computational Tools 361MLWF Generation 362References 3637 Methods for a Rapid and Automated Description of Proteins: Protein Structure, Protein Similarity, and Protein Folding 369Zhanyong Guo and Dieter CremerIntroduction 369Protein Structure Description Methods Based on Frenet Coordinates and/or Coarse Graining 373The Automated Protein Structure Analysis (APSA) 375The Curvature–Torsion Description for Idealized Secondary Structures 378Identification of Helices, Strands, and Coils 384Difference between Geometry?-Based and H?]Bond?-Based Methods 385Combination of Geometry?-Based and H?-Bond?]Based Methods 388Chirality of SSUs 388What is a Regular SSU? 389A Closer Look at Helices: Distinction between α- and 310?-Helices 391Typical Helix Distortions 395Level 2 of Coarse Graining: The Curved Vector Presentation of Helices 398Identification of Kinked Helices 402Analysis of Turns 406Introduction of a Structural Alphabet 409Derivation of a Protein Structure Code 411Description of Protein Similarity 416Qualitative and Quantitative Assessment of Protein Similarity 417The Secondary Code and Its Application in Connection with Protein Similarity 423Description of Protein Folding 423Concluding Remarks 426Acknowledgments 428References 428Index 439