Artificial Intelligence in Catalysis

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

Buch, Englisch, 272 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

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

Inhaltsverzeichnis

PART 1. MACHINE LEARNING APPLICATIONS IN STRUCTURAL ANALYSIS AND REACTION MONITORING
1) Computer Vision in Chemical Reaction Monitoring and Analysis
2) Machine Learning Meets Mass Spectrometry: a Focused Perspective
3) Application of Artificial Neural Networks in Analysis of Microscopy Data
 
PART 2. QUANTUM CHEMICAL METHODS MEET MACHINE LEARNING
4) Construction of Training Datasets for Chemical Reactivity Prediction Through Computational Means
5)Machine Learned Force Fields: Fundamentals, its Reach, and Challenges
 
PART 3. CATALYST OPTIMIZATION AND DISCOVERY WITH MACHINE LEARNING
6) Optimization of Catalysts using Computational Chemistry, Machine Learning, and Cheminformatics
7) Predicting Reactivity with Machine Learning
8) Predicting Selectivity in Asymmetric Catalysis with Machine Learning
9) Artificial Intelligence-assisted Heterogeneous Catalyst Design, Discovery, and Synthesis Utilizing Experimental Data

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 Senior Research Fellow at the National University of Singapore. His current research focuses on the automation of computational chemistry, machine learning for chemical applications, Born-Oppenheimer molecular dynamics modeling, and the theory of catalysis.