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A comprehensive account of how to use theoretical models to describe and predict key chemical parameters and phenomena, from electron transfer to bond strength, and from acid-base behavior to aromaticity.
A comprehensive account of how to use theoretical models to describe and predict key chemical parameters and phenomena, from electron transfer to bond strength, and from acid-base behavior to aromaticity.
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Dr. Shubin Liu is a Senior Computational Scientist at the Research Computing Center, University of North Carolina at Chapel Hill. He obtained his Ph.D. degree with Robert G. Parr in 1996 and postdoctoral training with Weitao Yang of Duke University. He has been an independent researcher since 2000, focusing on developing a chemical reactivity theory using density functional theory language. Dr. Shubin Liu has authored over 200 peer-reviewed publications and is recognized in the field by various scientific awards including the Wiley-IJQC Young Investigator Award.
Inhaltsangabe
1. Chemical Concepts from Molecular Orbital Theory 2. Chemical Concepts from Ab Initio Valence Bond Theory 3. Chemical Concepts from Conceptual Density Functional Theory 4. Chemical Concepts from Density-Based Approaches in Density Functional Theory 5. Chemical Bonding 6. Partial Charges 7. Atoms in Molecules 8. Effective Oxidation States Analysis 9. Aromaticity and Antiaromaticity 10. Acidity and Basicity 11. Sigma Hole Supported Interactions: Qualitative Features, Various Incarnations, and Disputations 12. On the Generalization of Marcus Theory for Two-State Photophysical Processes 13. Computational Modeling of CO2 Reduction and Conversion via Heterogeneous and Homogeneous Catalysis 14. Excited States in Conceptual DFT 15. Modeling the Photophysical Processes of Organic Molecular Aggregates with Inclusion of Intermolecular Interactions and Vibronic Couplings 16. Duality of Conjugated ¿¿ Electrons 17. Energy Decomposition Analysis and Its Applications 18. Chemical Concepts in Solids 19. Toward Interpretable Machine Learning Models for Predicting Spectroscopy, Catalysis, and Reactions 20. Learning Design Rules for Catalysts Through Computational Chemistry and Machine Learning 1. Chemical Concepts from Molecular Orbital Theory 2. Chemical Concepts from Ab Initio Valence Bond Theory 3. Chemical Concepts from Conceptual Density Functional Theory 4. Chemical Concepts from Density-Based Approaches in Density Functional Theory 5. Chemical Bonding 6. Partial Charges 7. Atoms in Molecules 8. Effective Oxidation States Analysis 9. Aromaticity and Antiaromaticity 10. Acidity and Basicity 11. Sigma Hole Supported Interactions: Qualitative Features, Various Incarnations, and Disputations 12. On the Generalization of Marcus Theory for Two-State Photophysical Processes 13. Computational Modeling of CO2 Reduction and Conversion via Heterogeneous and Homogeneous Catalysis 14. Excited States in Conceptual DFT 15. Modeling the Photophysical Processes of Organic Molecular Aggregates with Inclusion of Intermolecular Interactions and Vibronic Couplings 16. Duality of Conjugated ¿¿ Electrons 17. Energy Decomposition Analysis and Its Applications 18. Chemical Concepts in Solids 19. Toward Interpretable Machine Learning Models for Predicting Spectroscopy, Catalysis, and Reactions 20. Learning Design Rules for Catalysts Through Computational Chemistry and Machine Learning
1. Chemical Concepts from Molecular Orbital Theory 2. Chemical Concepts from Ab Initio Valence Bond Theory 3. Chemical Concepts from Conceptual Density Functional Theory 4. Chemical Concepts from Density-Based Approaches in Density Functional Theory 5. Chemical Bonding 6. Partial Charges 7. Atoms in Molecules 8. Effective Oxidation States Analysis 9. Aromaticity and Antiaromaticity 10. Acidity and Basicity 11. Sigma Hole Supported Interactions: Qualitative Features, Various Incarnations, and Disputations 12. On the Generalization of Marcus Theory for Two-State Photophysical Processes 13. Computational Modeling of CO2 Reduction and Conversion via Heterogeneous and Homogeneous Catalysis 14. Excited States in Conceptual DFT 15. Modeling the Photophysical Processes of Organic Molecular Aggregates with Inclusion of Intermolecular Interactions and Vibronic Couplings 16. Duality of Conjugated ¿¿ Electrons 17. Energy Decomposition Analysis and Its Applications 18. Chemical Concepts in Solids 19. Toward Interpretable Machine Learning Models for Predicting Spectroscopy, Catalysis, and Reactions 20. Learning Design Rules for Catalysts Through Computational Chemistry and Machine Learning 1. Chemical Concepts from Molecular Orbital Theory 2. Chemical Concepts from Ab Initio Valence Bond Theory 3. Chemical Concepts from Conceptual Density Functional Theory 4. Chemical Concepts from Density-Based Approaches in Density Functional Theory 5. Chemical Bonding 6. Partial Charges 7. Atoms in Molecules 8. Effective Oxidation States Analysis 9. Aromaticity and Antiaromaticity 10. Acidity and Basicity 11. Sigma Hole Supported Interactions: Qualitative Features, Various Incarnations, and Disputations 12. On the Generalization of Marcus Theory for Two-State Photophysical Processes 13. Computational Modeling of CO2 Reduction and Conversion via Heterogeneous and Homogeneous Catalysis 14. Excited States in Conceptual DFT 15. Modeling the Photophysical Processes of Organic Molecular Aggregates with Inclusion of Intermolecular Interactions and Vibronic Couplings 16. Duality of Conjugated ¿¿ Electrons 17. Energy Decomposition Analysis and Its Applications 18. Chemical Concepts in Solids 19. Toward Interpretable Machine Learning Models for Predicting Spectroscopy, Catalysis, and Reactions 20. Learning Design Rules for Catalysts Through Computational Chemistry and Machine Learning
Rezensionen
04 . Excited States in Conceptual DFT 15. Modeling the Photophysical Processes of Organic Molecular Aggregates with Inclusion of Intermolecular Interactions and Vibronic Couplings 16. Duality of Conjugated ¿¿ Electrons 17. Energy Decomposition Analysis and Its Applications 18. Chemical Concepts in Solids 19. Toward Interpretable Machine Learning Models for Predicting Spectroscopy, Catalysis, and Reactions 20. Learning Design Rules for Catalysts Through Computational Chemistry and Machine Learning
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