Reviews in Computational Chemistry / Reviews in Computational Chemistry, Volume 29

Contributors x Preface xii Contributors to Previous Volumes xv 1 Noncovalent Interactions in Density Functional Theory 1 Gino A. DiLabio and Alberto Otero-de-la-Roza Introduction 1 Overview of Noncovalent Interactions 3 Theory Background 9 Density?-Functional Theory 9 Failure of Conventional DFT for Noncovalent Interactions 17 Noncovalent Interactions in DFT 20 Pairwise Dispersion Corrections 20 Potential-Based Methods 42 Minnesota Functionals 47 Nonlocal Functionals 54 Performance of Density Functionals for Noncovalent Interactions 59 Description of Noncovalent Interactions Benchmarks 59 Performance of Dispersion-Corrected Methods 66 Noncovalent Interactions in Perspective 74 Acknowledgments 78 References 79 2 Long?-Range Interparticle Interactions: Insights from Molecular Quantum Electrodynamics (QED) Theory 98 Akbar Salam Introduction 98 The Interaction Energy at Long Range 101 Molecular QED Theory 104 Electrostatic Interaction in Multipolar QED 112 Energy Transfer 114 Mediation of RET by a Third Body 119 Dispersion Potential between a Pair of Atoms or Molecules 123 Triple-Dipole Dispersion Potential 128 Dispersion Force Induced by External Radiation 132 Macroscopic QED 136 Summary 141 References 143 3 Efficient Transition State Modeling Using Molecular Mechanics Force Fields for the Everyday Chemist 152 Joshua Pottel and Nicolas Moitessier Introduction 152 Molecular Mechanics and Transition State Basics 154 Molecular Mechanics 154 Transition States 157 Ground State Force Field Techniques 158 Introduction 158 ReaxFF 159 Reaction Force Field 161 Seam 163 Empirical Valence Bond/Multiconfiguration Molecular Dynamics 166 Asymmetric Catalyst Evaluation 169 TSFF Techniques 173 Introduction 173 Q2MM 175 Conclusion and Prospects 178 References 178 4 Machine Learning in Materials Science: Recent Progress and Emerging Applications 186 Tim Mueller, Aaron Gilad Kusne, and Rampi Ramprasad Introduction 186 Supervised Learning 188 A Formal Probabilistic Basis for Supervised Learning 189 Supervised Learning Algorithms 199 Unsupervised Learning 213 Cluster Analysis 215 Dimensionality Reduction 226 Selected Materials Science Applications 237 Phase Diagram Determination 237 Materials Property Predictions Based on Data from Quantum Mechanical Computations 240 Development of Interatomic Potentials 245 Crystal Structure Predictions (CSPs) 249 Developing and Discovering Density Functionals 250 Lattice Models 251 Materials Processing and Complex Materials Behavior 256 Automated Micrograph Analysis 257 Structure-Property Relationships in Amorphous Materials 260 Additional Resources 263 Summary 263 Acknowledgments 264 References 264 5 Discovering New Materials via A Priori Crystal Structure Prediction 274 Eva Zurek Introduction and Scope 274 Crystal Lattices and Potential Energy Surfaces 276 Calculating Energies and Optimizing Geometries 281 Methods to Predict Crystal Structures 282 Following Soft Vibrational Modes 283 Random (Sensible) Structure Searches 284 Simulated Annealing 285 Basin Hopping and Minima Hopping 287 Metadynamics 288 Particle Swarm Optimization 289 Genetic Algorithms and Evolutionary Algorithms 291 Hybrid Methods 292 The Nitty?-Gritty Aspects of Evolutionary Algorithms 294 Workflow 294 Selection for Procreation 295 Evolutionary Operators 297 Maintaining Diversity 299 The XtalOpt Evolutionary Algorithm 300 Practical Aspects of Carrying out an Evolutionary Structure Search 303 Crystal Structure Prediction at Extreme Pressures 312 Note in Proof 315 Conclusions 316 Acknowledgments 317 References 317 6 Introduction to Maximally Localized Wannier Functions 327 Alberto Ambrosetti and Pier Luigi Silvestrelli Introduction 327 Theory 329 Bloch States 329 Wannier Functions 331 Maximally Localized Wannier Functions: Gamma-Point Formulation 333 Extension to Brillouin-Zone k?]Point Sampling 336 Degree of WF Localization 337 Entangled Bands and Subspace Selection 338 Applications 340 Charge Visualization 340 Charge Polarization 344 Bonding Analysis 348 Amorphous Phases and Defects 351 Electron Transport 354 Efficient Basis Sets 356 Hints About MLWFs Numerical Computation 361 Brief Review of the Presently Available Computational Tools 361 MLWF Generation 362 References 363 7 Methods for a Rapid and Automated Description of Proteins: Protein Structure, Protein Similarity, and Protein Folding 369 Zhanyong Guo and Dieter Cremer Introduction 369 Protein Structure Description Methods Based on Frenet Coordinates and/or Coarse Graining 373 The Automated Protein Structure Analysis (APSA) 375 The Curvature-Torsion Description for Idealized Secondary Structures 378 Identification of Helices, Strands, and Coils 384 Difference between Geometry?-Based and H?]Bond?-Based Methods 385 Combination of Geometry?-Based and H?-Bond?]Based Methods 388 Chirality of SSUs 388 What is a Regular SSU? 389 A Closer Look at Helices: Distinction between alpha- and 310?-Helices 391 Typical Helix Distortions 395 Level 2 of Coarse Graining: The Curved Vector Presentation of Helices 398 Identification of Kinked Helices 402 Analysis of Turns 406 Introduction of a Structural Alphabet 409 Derivation of a Protein Structure Code 411 Description of Protein Similarity 416 Qualitative and Quantitative Assessment of Protein Similarity 417 The Secondary Code and Its Application in Connection with Protein Similarity 423 Description of Protein Folding 423 Concluding Remarks 426 Acknowledgments 428 References 428 Index 439