Titel:	Quantum chemistry in the age of machine learning
Mitwirkende:	Dral, Pavlo O. [HerausgeberIn]
Verf.angabe:	edited by Pavlo O. Dral (Professor, State Key Laboratory of Physical Chemistry of Solid Surfaces, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Department of Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, China)
Verlagsort:	Amsterdam
Verlag:	Elsevier
E-Jahr:	2023
Jahr:	[2023]
Umfang:	xvii, 682 Seiten
Illustrationen:	Illustrationen, Diagramme
Format:	229 mm
Fussnoten:	1. Very brief introduction to quantum chemistry; 2. Density functional theory; 3. Semiempirical quantum mechanical methods; 4. From small molecules to solid-state materials: A brief discourse on an example of carbon compounds; 5. Basics of dynamics; 6. Machine learning: An overview; 7. Unsupervised learning; 8. Neural networks; 9. Kernel methods; 10. Bayesian inference; 11. Potentials based on linear models; 12. Neural network potentials; 13. Kernel method potentials; 14. Constructing machine learning potentials with active learning; 15. Excited-state dynamics with machine learning; 16. Machine learning for vibrational spectroscopy; 17. Molecular structure optimizations with Gaussian process regression; 18. Learning electron densities; 19. Learning dipole moments and polarizabilities; 20. Learning excited-state properties; 21. Learning from multiple quantum chemical methods: ?-learning, transfer learning, co-kriging, and beyond; 22. Data-driven acceleration of coupled-cluster and perturbation theory methods; 23. Redesigning density functional theory with machine learning; 24. Improving semiempirical quantum mechanical methods with machine learning; 25. Machine learning wavefunction; 26. Analysis of nonadiabatic molecular dynamics trajectories; 27. Design of organic materials with tailored optical properties: Predicting quantum-chemical polarizabilities and derived Quantities
ISBN:	978-0-323-90049-2
Abstract:	Quantum chemistry is simulating atomistic systems according to the laws of quantum mechanics, and such simulations are essential for our understanding of the world and for technological progress. Machine learning revolutionizes quantum chemistry by increasing simulation speed and accuracy and obtaining new insights. However, for nonspecialists, learning about this vast field is a formidable challenge. Quantum Chemistry in the Age of Machine Learning covers this exciting field in detail, ranging from basic concepts to comprehensive methodological details to providing detailed codes and hands-on tutorials. Such an approach helps readers get a quick overview of existing techniques and provides an opportunity to learn the intricacies and inner workings of state-of-the-art methods. The book describes the underlying concepts of machine learning and quantum chemistry, machine learning potentials and learning of other quantum chemical properties, machine learning-improved quantum chemical methods, analysis of Big Data from simulations, and materials design with machine learning. Drawing on the expertise of a team of specialist contributors, this book serves as a valuable guide for both aspiring beginners and specialists in this exciting field.- Compiles advances of machine learning in quantum chemistry across different areas into a single resource- Provides insights into the underlying concepts of machine learning techniques that are relevant to quantum chemistry- Describes, in detail, the current state-of-the-art machine learning-based methods in quantum chemistry
Schlagwörter:	(s)Quantenchemie    / (s)Maschinelles Lernen    / (s)Wissenschaftliches Rechnen
Sprache:	eng
RVK-Notation:	VE 5650
K10plus-PPN:	1822171997