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.
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
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