Practical aspects of computational chemistry V / / Jerzy Leszczynski, Manoj K. Shukla, editors

• Record Nr.: UNINA9910523903803321
• Pubbl/distr/stampa: Cham, Switzerland : , : Springer, , [2022]; ©2022
• ISBN: 3-030-83244-9
• Descrizione fisica: 1 online resource (292 pages)
• Disciplina: 541.0285
• Soggetti: Chemistry, Physical and theoretical - Data processing
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Preface -- Contents -- 1 Introductory Roadmap to Current Reactive Force-Field Methodologies -- 1.1 Introduction -- 1.2 Bond Order Methodologies -- 1.3 Valence Bond Models -- 1.4 Parameterization -- 1.5 Other Reactive Methods -- 1.6 Conclusion -- References -- 2 Physics-Based Coarse-Grained Modeling in Bio-and Nanochemistry -- 2.1 Introduction -- 2.2 Designing Coarse-Grained Models and Force Fields -- 2.2.1 Potential of Mean Force as the Origin of CG Force Fields -- 2.2.2 Derivation of CG Force Fields from PMF Surfaces -- 2.2.3 Solvent Treatment in CG Force Fields -- 2.2.4 Force-Field Parameterization -- 2.2.4.1 Direct Computation of PMF Surfaces -- 2.2.4.2 Force Matching -- 2.2.4.3 Iterative Boltzmann Inversion and Inverse Monte Carlo Iteration -- 2.2.4.4 Fitting Force-Field Parameters to Reproduce the Experimental Data -- 2.2.4.5 Calibration with Experimental Structures -- 2.3 Methods of Conformational Search -- 2.3.1 Canonical Monte Carlo -- 2.3.2 Molecular Dynamics -- 2.3.3 Extensions of MC and MD -- 2.3.4 Global Energy Minimization -- 2.3.5 Use of Geometrical Restraints in CG Simulations -- 2.3.5.1 Chemical Cross-Link Mass-Spectroscopy and Fluorescence Energy Transfer -- 2.3.5.2 Small Angle X-ray/Neutron Scattering -- 2.3.5.3 Mutagenesis and Hydrogen-Deuterium Exchange (HDX) -- 2.3.5.4 Nuclear Magnetic Resonance -- 2.3.6 Contact-Distance and Template-Based Restraints -- 2.4 Examples of Physics-Based CG Force Fields and Their Applications -- 2.4.1 AWSEM -- 2.4.2 MARTINI -- 2.4.3 OPEP and ; HiRe-RNA -- 2.4.4 oxDNA and oxRNA -- 2.4.5 SIRAH -- 2.4.6 UNICORN -- 2.4.7 Structure-Based and Elastic-Network Potentials -- 2.5 Conclusions and Outlook -- References -- 3 First-Principles Modeling of Non-covalent Interactions in Molecular Systems and Extended Materials -- 3.1 Introduction -- 3.2 Theoretical Models of Non-covalent Interactions.; 3.2.1 Modeling van der Waals Interactions -- 3.2.2 Quantum Chemical Approaches for Non-covalent Interactions -- 3.2.3 Dispersion Computations in DFT -- 3.2.4 Dispersion Computation Through MP2 and Higher Correlation Methods -- 3.3 Non-covalent Interactions in Hydrogen-Bonded (HB) Systems -- 3.3.1 Hydrogen Bonding and Related Properties of Small Water Clusters -- 3.3.2 Nature of O-H Stretching Modes -- 3.3.3 Effect of Halide Ion Interactions with Small Water Clusters -- 3.3.4 CTTS Properties of Halide-Water Clusters -- 3.3.5 Effect of Low-Frequency Vibrations of HBs in Fatty Acid Dimers and Their Amides -- 3.3.6 Empirical Additive Relations of ΔEB for Fatty Acid and Amide Dimers -- 3.4 Molecular Modeling of Strong and Weak Cation-π Interactions -- 3.5 Molecular Modeling of π-π Interactions -- 3.6 Modeling Non-covalent Interactions in Bio-inspired Supramolecular Systems -- 3.7 Conclusions -- References -- 4 DNA Damage Radiosensitizers Geared Towards HydratedElectrons -- 4.1 Introduction -- 4.2 Nucleoside Derivatives: A Trojan Horse Approach to Radiotherapy -- 4.2.1 Modified Nucleosides as Radiosensitizers -- 4.2.2 Nucleoside Derivatives as Trojan Horses -- 4.2.3 Electron-Induced Degradation of Modified Nucleosides: Experimental Studies -- 4.3 Computational Studies on Nucleoside Radiosensitizers -- 4.3.1 5-Bromo-2-Deoxyuridine -- 4.3.2 Crucial Characteristic of Electron Attachment Process -- 4.3.3 Bromonucleobases -- 4.3.4 5-Substituted Uracils as Potential Radiosensitizers -- 4.3.5 A Need to Expand the Computational Model for Difficult Derivatives -- 4.4 Oxygen Mimetics -- 4.5 Metallic Nanoparticles and Metal Complexes -- 4.5.1 Possible Mechanisms of Radiosensitization -- 4.5.2 On the Role of Low Energy Electrons in the Radiosensitization of DNA by Metallic Nanoparticles and Complexes -- 4.5.3 Not only Gold Nanoparticles and Cisplatin -- 4.6 Summary.; References -- 5 Application of Computational Chemistry for Contaminant Adsorption on the Components of Soil Surfaces -- 5.1 Introduction -- 5.2 Density Functional Theory (DFT) -- 5.2.1 Preliminaries -- 5.2.2 Bloch Function -- 5.2.2.1 Bypassing Periodicity: Cluster Models -- 5.2.3 K-point Sampling -- 5.2.4 Density of States (DOS) and Analysis of Orbitals -- 5.2.5 Self-Interaction Errors -- 5.2.6 The Problem of Electron Correlation -- 5.2.7 Forces, Hellmann-Feynman Theorem, and Geometry Optimization -- 5.3 Case Study: Adsorption of Munitions in Soils -- 5.3.1 Binding Energies -- 5.3.2 Cluster Models and Electrochemical Properties -- 5.3.3 Comparison of Cluster and Periodic Surface Models -- 5.3.4 Lewis Acidity and Environmental Fate -- 5.3.5 Environmental Transport -- 5.4 Looking to the Future -- 5.4.1 Breathing New Life into an Old Method: Density Functional Tight Binding -- 5.4.2 Artificial Intelligence and Machine Learning (AI/ML) -- 5.4.2.1 Machine Learning and Energetics -- 5.4.2.2 AI/ML and DFT -- 5.5 Conclusions -- References -- 6 Application of Computational Approaches to Analysis of Multistep Chemical Reactions of Energetic Materials: Hydrolysis of Hexahydro-1,3,5-Trinitro-1,3,5-Triazine (RDX) and Octahydro-1,3,5,7-Tetranitro-1,3,5,7-Tetrazocine (HMX) -- 6.1 Introduction -- 6.1.1 Short Survey of Experimental Data on RDX Hydrolysis -- 6.1.2 Short Survey of Experimental Data of HMX Hydrolysis -- 6.2 Computational Modeling of Hydrolysis of RDX -- 6.2.1 Conformational Analysis of RDX Structure -- 6.2.2 Mechanism of ; RDX Alkaline Hydrolysis -- 6.2.3 Kinetics of RDX Alkaline Hydrolysis -- 6.2.4 Hydrolysis of HMX -- 6.2.4.1 Conformational Analysis of HMX Structure -- 6.2.4.2 Mechanism of HMX Alkaline Hydrolysis -- 6.2.4.3 Kinetics of HMX Alkaline Hydrolysis -- 6.2.5 Mechanism of 4-NDAB Decomposition Under Alkaline Conditions -- 6.3 Conclusion.; References -- 7 Dataset Modelability by QSAR: Continuous Response Variable -- Abbreviations -- 7.1 Introduction -- 7.2 Methods -- 7.2.1 Datasets -- 7.2.1.1 Training Set Data -- 7.2.1.2 Test Set Data (14 Datasets) -- 7.2.2 Modelability Criteria -- 7.3 Results and Discussion -- 7.4 Conclusions -- References -- 8 A Cluster Model for Interpretation of Surface-Enhanced Raman Scattering of Organic Compounds Interacting with Silver Nanoparticles -- Abbreviations -- 8.1 Introduction -- 8.2 Surface-Enhanced Raman Scattering (SERS) Experiment -- 8.2.1 General Aspects -- 8.2.2 Electromagnetic Mechanism of SERS (EM) -- 8.2.3 Chemical Enhancement Mechanism (CE) -- 8.2.4 Use of Silver Nanoparticles in SERS Applications -- 8.3 Geometrical and Electronic Structures of Silver Clusters -- 8.4 Detection of Single Molecules Using SERS Technique -- 8.5 Concluding Remarks -- References -- Index.