Computational chemistry Books

Table of Contents1. Overview 2. Five Important Equations in Thermodynamics 3. Gibbs Free Energy, Work, and Equilibrium 4. Thermodynamics of the Gas State 5. Thermodynamics of the Liquid State 6. Solid State 7. Quantum Principles 8. Quantum Systems With Constant Potential 9. Quantum Energies for Central Potentials 10. Electronic and Nuclear States 11. Rotation–Vibration Spectra 12. Classical Statistical Mechanics 13. Quantum Statistical Mechanics 14. Nonequilibrium Thermodynamics 15. Reaction Rates and Mechanisms

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Book SynopsisChemoinformatics draws upon techniques from many disciplines including computer science, mathematics, computational chemistry and data visualisation to tackle these problems.Trade ReviewThis is a rather remarkable little book. Just as its title promises, it provides an introduction to a very wide variety of chemoinformatic and CADD topics. Allthough the book is written for beginning graduate students and advanced undergraduates, it could certainly provide a very helpful overview for professionals with no prior knowledge of the subject matter. Robert S. Pearlman, University of Texas at Austin. J.Am. Chem. Soc., Vol 126, No. 4, 2004.Table of ContentsPreface. Acknowledgements. 1: Representation and Manipulation of 2D Molecular Structures. 1. Introduction. 2. Computer Representations of Chemical Structures. 3. Structure Searching. 4. Substructure Searching. 5. Reaction Databases. 6. The Representation of Patents and Patent Databases. 7. Relational Database Systems. 8. Summary. 2: Representation and Manipulation of 3D Molecular Structures. 1. Introduction. 2. Experimental 3D databases. 3. 3D Pharmacophores. 4. Implementation of 3D database Searching. 5. Theoretical 3D Databases. 6. Methods to Derive 3D Pharmacophores. 7. Applications of 3D Pharmacophore Mapping and 3D Database Searching. 8. Summary. 3: Molecular Descriptors. 1. Introduction. 2. Descriptors Calculated from the 2D Structure. 3. Descriptors Based on 3D Representations. 4. Data Verification and Manipulation. 5. Summary. 4: Computational Models. 1. Introduction. 2. Historical Overview. 3. Deriving a QSAR Equation: Simple and Multiple Linear Regression. 4. Designing a QSAR 'Experiment'. 5. Principal Components Regression. 6. Partial Least Squares. 7. Molecular Field Analysis and Partial Least Squares. 8. Summary. 5: Similarity Methods. 1. Introduction. 2. Similarity Based on 2D Fingerprints. 3. Similarity Coefficients. 4. Other 2D Descriptor Methods. 5. 3D Similarity. 6. Summary. 6: Selecting Diverse SetsOf Compounds. 1. Introduction. 2. Cluster Analysis. 3. Dissimilarity-Based selection methods. 4. Cell-Based Methods. 5. Optimisation Methods. 6. Comparison and Evaluation of Selection Methods. 7. Summary. 7: Analysis of High-Throughput Screening Data. 1. Introduction. 2. Data Visualisation. 3. Data Mining Methods. 4. Summary. 8: Virtual Screening. 1. Introduction. 2. 'Drug-Likeness' and Compound Filters. 3. Structure-Based Virtual Screening. 4. The Prediction of ADMET Properties. 5. Summary. 9: Combinatorial Chemistry and Library Design. 1. Introduction. 2. Diverse and Focussed Libraries. 3. Library Enumeration. 4. Combinatorial Library Design Strategies. 5. Approaches to Product-Based Library Design. 6. Multiobjective Library Design. 7. Practical Examples of Library Design. 8. Summary. Appendix 1: Matrices, Eigenvectors and Eigenvalues. Appendix 2: Conformation, Energy Calculations and Energy Surfaces. Further Reading. References. Index.

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Book SynopsisNew technologies are made possible by new materials, and until recently new materials could only be discovered experimentally. Recent advances in solving the crystal structure prediction problem means that the computational design of materials is now a reality. Computational Materials Discovery provides a comprehensive review of this field covering different computational methodologies as well as specific applications of materials design. The book starts by illustrating how and why first-principle calculations have gained importance in the process of materials discovery. The book is then split into three sections, the first exploring different approaches and ideas including crystal structure prediction from evolutionary approaches, data mining methods and applications of machine learning. Section two then looks at examples of designing specific functional materials with special technological relevance for example photovoltaic materials, superconducting materials, topological insulators and thermoelectric materials. The final section considers recent developments in creating low-dimensional materials. With contributions from pioneers and leaders in the field, this unique and timely book provides a convenient entry point for graduate students, researchers and industrial scientists on both the methodologies and applications of the computational design of materials.Table of ContentsComputational Materials Discovery: Dream or Reality?; Computational Materials Discovery Using Evolutionary Algorithms; Applications of Machine Learning for Representing Interatomic Interactions; Embedding Methods in Materials Discovery; Chemical Bonding Investigations for Materials; Computational Design of Photovoltaic Materials; First-Principles Computational Approaches to Superconducting Transition Temperatures: Phonon-Mediated Mechanism and Beyond; Quest for New Thermoelectric Materials; Rational Design of Polymer Dielectrics: An Application of Density Functional Theory and Machine Learning; Rationalising and Predicting the Structure and Bonding of Bare and Ligated Transition Metal Clusters and Nanoparticles; Recent Advances in the Theory of Non-carbon Nanotubes; Discovery of Novel Topological Materials Via High-throughput Computational Search; Computational Discovery of Organic LED Materials

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Book SynopsisAs analysis, in terms of detection limits and technological innovation, in chemical and biological fields has developed so computational techniques have advanced enabling greater understanding of the data. Indeed, it is now possible to simulate spectral data to an excellent level of accuracy, allowing chemists and biologists access to robust and reliable analytical methodologies both experimentally and theoretically. This work will serve as a definitive overview of the field of computational simulation as applied to analytical chemistry and biology, drawing on recent advances as well as describing essential, established theory. Computational approaches provide additional depth to biochemical problems, as well as offering alternative explanations to atomic scale phenomena. Highlighting the innovative and wide-ranging breakthroughs made by leaders in computational spectrum prediction and the application of computational methodologies to analytical science, this book is for graduates and postgraduate researchers showing how computational analytical methods have become accessible across disciplines. Contributed chapters originate from a group of internationally-recognised leaders in the field, each applying computational techniques to develop our understanding of and supplement the data obtained from experimental analytical science.Table of ContentsUnivariate and Multivariate Statistical Approaches to the Analysis and Interpretation of NMR-based Metabolomics Datasets of Increasing Complexity; Recent Advances in Computational NMR Spectrum Prediction; Computational Vibrational Spectroscopy: A Contemporary Perspective; Isotope Effects as Analytical Probes: Application of Computational Theory; Applications of Computational Intelligence Techniques in Chemical and Biochemical Analyiss; Computational Spectroscopy and Photophysics in Complex Biological Systems: Towards an in silico Photobiology; Bridging the Gap Between Atomistic Molecular Dynamics Simulations and Wet-lab Experimental Techniques: Applications to Membrane Proteins; Solid State Chemistry: Computational Chemical Analysis for Materials Science; Electron Spin Resonance for the Detection of Paramagnetic Species: from Fundamentals to Computational Methods for Simulation and Interpretation

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Book SynopsisMetabolomics and proteomics allow deep insights into the chemistry and physiology of biological systems. This book expounds open-source programs, platforms and programming tools for analysing metabolomics and proteomics mass spectrometry data. In contrast to commercial software, open-source software is created by the academic community, which facilitates the direct interaction between users and developers and accelerates the implementation of new concepts and ideas. The first section of the book covers the basics of mass spectrometry, experimental strategies, data operations, the open-source philosophy, metabolomics, proteomics and statistics/ data mining. In the second section, active programmers and users describe available software packages. Included tutorials, datasets and code examples can be used for training and for building custom workflows. Finally, every reader is invited to participate in the open science movement.Table of ContentsIntroduction; Mass Spectrometry Data Operations and Workflows; Metabolomics; Proteomics; Statistics, Data Mining and Modeling; OpenMS and KNIME for Mass Spectrometry Data Processing; Metabolomics Data Analysis Using MZmine; Pre-processing and Analysis of Metabolomics Data with XCMS/R and XCMS Online; Statistical Evaluation and Integration of Multi-omics Data with MetaboAnalyst; Modular metaX Pipeline for Processing Untargeted Metabolomics Data; Metabolite Annotation With CEU Mass Mediator; Metabolite Annotation Using In Silico Generated Compounds: MINE and BioTransformer; Trans-Proteomic Pipeline for the Identification, Validation, and Quantification of Proteins; Quantitative Proteomics Data Analysis with PANDA, LFAQ and PANDA-view; Proteomic Workflows with R/R Markdown; Python in Proteomics; Mass Spectrometry Development Kit (MSDK): a Java Library for Mass Spectrometry Data Processing; MASSyPup64: Linux Live System for Mass Spectrometry Data Processing; Cross-platform Software Development and Distribution with Bioconda and BioContainers; Concluding Remarks and Perspectives

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Book SynopsisThe areas of synthesis and catalysis are largely driven by non-covalent interactions, and it is therefore essential to understand, control, and manipulate them. Doing so would allow for the optimisation of the properties and functions of new catalysts across the length scales. The current challenges in this area include structure determination of reactive intermediates, ascertaining structure-activity relationships, modelling transient states in catalytic cycles, and developing processes for reliable synthesis of non-covalent systems. The format of Faraday Discussions facilitates in-depth, dedicated discussions between researchers from across the area of synthesis and catalysis. This allows for a wide range of valuable insights and perspectives on the leading areas of the field. This volume brings together internationally leading researchers in synthesis, materials and catalysis, particularly involving systems where non-covalent interactions are crucial. In this volume the topics covered include: The importance of non-covalent interactions in synthesis Understanding the structural and electronic changes within these catalytic systems Modelling and computational analysis of reactive sites Controlling the activity and selectivity of a synthetic catalyst by manipulation of the surroundingsTable of ContentsThe importance of non-covalent interactions in synthesis;Understanding the structural and electronic changes within these catalytic systems;Modelling and computational analysis of reactive sites;Controlling the activity and selectivity of a synthetic catalyst by manipulation of the surroundings

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Book SynopsisElectric-field-mediated chemistry is an emerging topic that is rapidly growing and fanning out in many directions. It involves theoretical and experimental aspects, as well as intense interplay between them, including breakthrough achievements such as the proof-of-principle that a Diels–Alder reaction, which involves two simultaneous C–C bond making events, can be catalysed or inhibited simply by changing the direction of an oriented external-electric field (OEEF). This productive interplay between the theoretical and experimental branches of chemistry is continuing, and gradually defining a new sub-field wherein various sources of electric fields, whether external or built-in and designed, or even surface induced fields (plasmons), are brought to bear on chemical reactions, molecular structures, and nano-systems, leading to control of reactivity, selectivity, chirality, molecular orientations, changes in structure, and in dynamics. Written by leaders in the field, Effects of Electric Fields on Structure and Reactivity is the first book on this exciting topic. Starting with an overview of the theory behind – and demonstrations of the effect of – electric fields on structure and reactivity, this accessible reference work aims to encourage those new to the field to consider harnessing these effects in their own work. Covering applications and recent theoretical developments, it is a useful resource for theoretical chemists and experimentalists alike.Table of ContentsIntroduction; The Impact of Electric Fields on Chemical Structure and Reactivity; Experimentally Harnessing Electric Fields in Chemical Transformations; Recent Advances in Designed Local Electric Fields; Principles of Molecular Devices Operated by Electric Fields; Computational Generation and Quantification of Electric Fields and Electrostatics-mediated Catalyst Optimization; Electrostatic Fields in Biophysical Chemistry; Molecular Dynamics in the Presence of External Electric Fields; Manipulation of Molecules by Combined Permanent and Induced Dipole Forces; Cavity-modified Chemistry: Towards Vacuum-field Catalysis; An Introduction to Laser-fields Effects on Chemical Reactivity

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Book SynopsisOver the past decade, great strides have been taken in developing methodologies that can treat more and more complex nano- and nano-bio systems embedded in complex environments. Multiscale Dynamics Simulations covers methods including DFT/MM-MD, DFTB and semi-empirical QM/MM-MD, DFT/MMPOL as well as Machine-learning approaches to all of the above. Focusing on key methodological breakthroughs in the field, this book provides newcomers with a comprehensive menu of multiscale modelling options so that they can better chart their course in the nano/bio world.Table of ContentsQM/MM with Auxiliary DFT in deMon2k; Computational Enzymology: A Challenge for Multiscale Approaches; QM/MM Simulations of Proteins: Is Explicit Inclusion of Polarization on the Horizon?; Electron and Molecular Dynamics Simulations with Polarizable Embedding; DFTB and Hybrid-DFTB Schemes: Application to Metal Nanosystems, Isolated and in Environments; From Atomic Orbitals to Nano-scale Charge Transport with Mixed Quantum/Classical Non-adiabatic Dynamics: Method, Implementation and Application; Modeling Nanocatalytic Reactions with DFTB/MM-MD and DFTB/NMD; Hohenberg–Kohn Theorems as a Basis for Multi-scale Simulations: Frozen-density Embedding Theory; 3D-RISM-KH Molecular Solvation Theory; Free Energy Analysis Algorithms along Transition Paths and Transmembrane Ion Permeation; Pathways in Classification Space: Machine Learning as a Route to Predicting Kinetics of Structural Transitions in Atomic Crystals; Machine Learning Algorithms for the Analysis of Molecular Dynamics Trajectories

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Book SynopsisTypical timelines to go from discovery to impact in the advanced materials sector are between 10 to 30 years. Advances in robotics and artificial intelligence are poised to accelerate the discovery and development of new materials dramatically. This book is a primer for any materials scientist looking to future-proof their careers and get ahead of the disruption that artificial intelligence and robotic automation is just starting to unleash. It is meant to be an overview of how we can use these disruptive technologies to augment and supercharge our abilities to discover new materials that will solve world’s biggest challenges. Written by world leading experts on accelerated materials discovery from academia (UC Berkeley, Caltech, UBC, Cornell, etc.), industry (Toyota Research Institute, Citrine Informatics) and national labs (National Research Council of Canada, Lawrence Berkeley National Labs).

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