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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.

Produktbeschreibung
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.
Autorenporträt
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.
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