Artificial Intelligence in Catalysis

Experimental and Computational Methodologies
1. Auflage 2025
ISBN: 978-3-527-35385-9
Verlag: Wiley-VCH GmbH

Buch, Englisch, 520 Seiten, Format (B × H): 170 mm x 244 mm

Enables researchers and professionals to leverage machine learning tools to optimize catalyst design and chemical processes
Artificial Intelligence in Catalysis delivers a state-of-the-art overview of artificial intelligence methodologies applied in catalysis. Divided into three parts, it covers the latest advancements and trends for catalyst discovery and characterization, reaction predictions, and process optimization using machine learning, quantum chemistry, and cheminformatics.
Written by an international team of experts in the field with each chapter combining experimental and computational knowledge, Artificial Intelligence in Catalysis includes information on: - Artificial intelligence techniques for chemical reaction monitoring and structural analysis
- Application of artificial neural networks in the analysis of electron microscopy data
- Construction of training datasets for chemical reactivity prediction through computational means
- Catalyst optimization and discovery using machine learning models
- Predicting selectivity in asymmetric catalysis with machine learning

Artificial Intelligence in Catalysis is a practical guide for researchers in academia and industry interested in developing new catalysts, improving organic synthesis, and minimizing waste and energy use.

Autoren/Hrsg.

Ananikov, Valentine P.

Polynski, Mikhail V.

Fachgebiete

• Naturwissenschaften Chemie Chemie Allgemein Chemometrik, Chemoinformatik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Naturwissenschaften Chemie Physikalische Chemie Chemische Reaktionen, Katalyse
• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Verfahrenstechnik, Chemieingenieurwesen

Inhaltsverzeichnis

PART 1: MACHINE LEARNING AND DEEP LEARNING METHODOLOGIES IN EXPERIMENTAL RESEARCH
1. Prediction of 1H and 13C NMR Data Using Artificial Intelligence
2. Combining Kinetic Data and ML for Elucidating Reaction Mechanisms
3. Machine Learning for Mass Spectrometry Automation
4. Reaction Monitoring Augmentation with Computer Vision Approaches
5. Automated Operando Reaction Monitoring
6. Reaction Rate Estimation with Machine Learning
7. Application of Artificial Neural Networks in the Analysis of Electron Microscopy Data
8. Chemical Reaction Networks

PART 2: COMBINING QUANTUM CHEMICAL METHODS WITH MACHINE LEARNING IN CATALYSIS
9. ML-Enabled Catalyst Discovery
10. Computationally-Led Catalyst Design within Asymmetric Organocatalysis
11. Combining Computational Chemistry and Machine Learning in Mechanistic Studies involving Transition Metal Complexes
12. Combining Computational Chemistry and Machine Learning in Mechanistic Studies of Heterogeneous Catalysts
13. Machine Learning Methodologies for Heterogeneous Catalysis and Materials Science
14. Machine Learning Models for Cross-Coupling Catalyst Prediction
15. Predicting Catalyst Activity with Machine Learning
16. The Design of Interatomic Potential Models for Heterogeneous Catalysts
17. Predicting Properties of Catalytic Transition Metal Complexes with Machine Learning
18. Combining Computational Chemistry and Machine Learning for the Prediction of Catalytic Activity of Transition Metal Complexes
19. Predicting Activation Barriers with Machine Learning Models
20. Machine Learning Applications in the Computational Research of Transition Metal Complexes
21. Machine Learning-Driven Optimization of Heterogeneous Catalysts
22. Desiphering Mechanisms of Heterogeneous Catalytic Reactions with Machine Learning
23. Machine Learning Potentials for Simulations of Catalysts and Materials

PART 3: CATALYST OPTIMIZATION WITH MACHINE LEARNING AND CATALYST RESEARCH AUTOMATION
24. Machine Learning in Reaction Optimization
25. Ligand Optimization with Machine Learning Techniques
26. Self-Driving Laboratories in Catalyst Research

Über Autor(innen)

Valentine P. Ananikov is a Professor and Laboratory Head at the Zelinsky Institute of Organic Chemistry at the Russian Academy of Sciences in Moscow, Russia. His research interests are focused on the development of new concepts in transition metal and nanoparticle catalysis, sustainable organic synthesis, and new methodologies for mechanistic studies of complex chemical transformations.
Mikhail V. Polynski is a Research Fellow at the National University of Singapore. His research interests are focused on the automation of computational chemistry research, supervised machine learning for chemical applications, Born-Oppenheimer molecular dynamics modeling, and the theory of catalysis.