Reviews in Computational Chemistry, Volume 29

Abby L. Parrill, Kenny B. Lipkowitz (Herausgeber)

Buch | Hardcover

480 Seiten

2016
John Wiley & Sons Inc (Verlag)
978-1-119-10393-6 (ISBN)

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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 29 include:

Noncovalent Interactions in Density-Functional Theory
Long-Range Inter-Particle Interactions: Insights from Molecular Quantum Electrodynamics (QED) Theory
Efficient Transition-State Modeling using Molecular Mechanics Force Fields for the Everyday Chemist
Machine Learning in Materials Science: Recent Progress and Emerging Applications
Discovering New Materials via a priori Crystal Structure Prediction
Introduction to Maximally Localized Wannier Functions
Methods for a Rapid and Automated Description of Proteins: Protein Structure, Protein Similarity, and Protein Folding

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 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: Γ-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 α- 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

Erscheint lt. Verlag	27.5.2016
Reihe/Serie	Reviews in Computational Chemistry ; 29
Verlagsort	New York
Sprache	englisch
Maße	165 x 241 mm
Gewicht	816 g
Themenwelt	Medizin / Pharmazie ► Studium ► 1. Studienabschnitt (Vorklinik)
	Naturwissenschaften ► Chemie ► Physikalische Chemie
	Naturwissenschaften ► Chemie ► Technische Chemie
	Naturwissenschaften ► Physik / Astronomie ► Quantenphysik
	Technik ► Maschinenbau
	Technik ► Umwelttechnik / Biotechnologie
ISBN-10	1-119-10393-2 / 1119103932
ISBN-13	978-1-119-10393-6 / 9781119103936
Zustand	Neuware