Artificial Intelligence in Catalysis: Experimental and Computational Methodologies

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Valentine P. Ananikov (Russian Academy of Sciences, Moscow, Russia) Mikhail V. Polynski (National University of Singapore, Singapore)

Artificial Intelligence in Catalysis

Experimental and Computational Methodologies

Valentine P. Ananikov (Russian Academy of Sciences, Moscow, Russia), Mikhail V. Polynski (National University of Singapore, Singapore)

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English

Blackwell Verlag GmbH
27 August 2025

Catalysis; Artificial intelligence

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.

Edited by: Valentine P. Ananikov (Russian Academy of Sciences Moscow Russia), Mikhail V. Polynski (National University of Singapore, Singapore)
Imprint: Blackwell Verlag GmbH
Country of Publication: Germany
Dimensions: Height: 244mm, Width: 170mm, Spine: 15mm
Weight: 680g
ISBN: 9783527353859
ISBN 10: 3527353852
Pages: 272
Publication Date: 27 August 2025
Audience: Professional and scholarly , Undergraduate
Format: Hardback
Publisher's Status: Active

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

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.