Annual Reports on Computational Chemistry

1st Edition, Volume 20 - November 15, 2024
Editor: Angela K. Wilson
Language: English
Hardback ISBN:
9 7 8 - 0 - 4 4 3 - 2 9 4 3 6 - 5
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 2 9 4 3 7 - 2

Annual Reports on Computaional Chemistry, Volume 20 highlights new advances in the field, with this new volume presenting interesting chapters written by an international board of… Read more

Annual Reports on Computational Chemistry

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Annual Reports on Computaional Chemistry, Volume 20 highlights new advances in the field, with this new volume presenting interesting chapters written by an international board of authors.

Cover image
Title page
Table of Contents
Copyright
Contributors
Preface
Chapter One: Density functional theory for van der Waals complexes: Size matters
Abstract
1 Overview of dispersion in DFT
2 Present state-of-the-art in noncovalent DFT
3 Understanding dispersion in DFT calculations
4 Results for large systems
5 The future of noncovalent DFT
Notes
Acknowledgements
References
Chapter Two: Towards reliable beyond-zero-sum density functional theory: Wavefunction-inspired DFT meets DFT-inspired wavefunctions
Abstract
1 Introduction
2 Formal roots: wavefunction theory, DFT, and adiabatic projections
3 Our general formalism
4 Kohn-Sham DFT and global hybrids
5 Range-separated hybrids and range-separated wavefunction+DFT
6 Perdew-Zunger self-interaction correction
7 CAS-in-DFT
8 DFT+U
9 Summary
References
Chapter Three: Super-ccCA: Robust transition metal thermochemistry
Abstract
1 Main group species
2 Transition metals: challenges
3 Transition metals: solutions
4 Conclusions
Acknowledgments
References
Chapter Four: Accessible and predictable QM-cluster model building for enzymes with the Residue Interaction Network Residue Selector
Abstract
1 Introduction and motivation
2 Current issues in QM-cluster modeling
3 The Residue Interaction Network ResidUe Selector (RINRUS) toolkit
4 Usage of RINRUS
5 Summary
References
Chapter Five: Recent advances in X-ray absorption near edge structure (XANES) simulations for catalysis: Theories and applications
Abstract
1 Introduction
2 Theories and methods
3 Applications
4 Conclusion and outlook
Acknowledgment
References
Chapter Six: Machine learning and deep learning based scoring functions in deciphering ligand-receptor binding: An application in drug design for GPCRs
Abstract
1 Introduction
2 Machine learning and deep learning scoring functions in drug design
3 Physics-based scoring functions
4 Ligand-residue interaction profile (LRIP) model
5 Application of LRIP in GPCR drug design
6 Summary and outlook
Acknowledgements
References
Chapter Seven: Toward AI/ML-assisted discovery of transition metal complexes
Abstract
1 Introduction
2 The design of 3D novel structures
3 Machine learning potentials for transition metal complexes
4 Data availability
5 Discussion and outlook
Acknowledgments
References
Chapter Eight: Federated quantum machine learning for drug discovery and healthcare
Abstract
1 Introduction
2 Quantum computing (QC) for drug discovery and development
3 Federated learning (FL)
4 Quantum federated learning (QFL)
5 Concluding remarks
Acknowledgments
References

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Annual Reports on Computational Chemistry

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