Artificial Intelligence in Catalysis E-Book
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- Sprache: Englisch
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.
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Seitenzahl: 534
Veröffentlichungsjahr: 2025
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Table of Contents
Cover
Table of Contents
Title Page
Copyright
Preface: Shaping the Future of Catalysis Research with Artificial Intelligence
Part I: Machine Learning Applications in Structural Analysis and Reaction Monitoring
Chapter 1: Computer Vision in Chemical Reaction Monitoring and Analysis
1.1 Introduction
1.2 Fundamentals of Computer Vision in Chemistry
1.3 Computer Vision and Machine Learning in Chemistry
1.4 Summary and Conclusion
References
Chapter 2: Machine Learning Meets Mass Spectrometry: A Focused Perspective
2.1 Introduction
2.2 Mass Spectrometry in the Machine Learning Era
2.3 Mass Spectrometry Methods Landscape and Their Potential for Machine Learning Applications
2.4 Representative Mass Spectrometry Applications of Machine Learning
2.5 Protocol for Solving General Problems in Mass Spectrometry Using Machine Learning
2.6 Summary and Conclusion
References
Chapter 3: Application of Artificial Neural Networks in the Analysis of Microscopy Data
3.1 Introduction
3.2 Deep Machine Learning for Image Analysis
3.3 iOk Platform for Automatic Image Analysis
3.4 Analysis of TEM Images of Heterogeneous Catalyst by iOk Platform
3.5 Practical Summary
3.6 Future Prospects
3.7 Acknowledgments
References
Part II: Quantum Chemical Methods Meet Machine Learning
Chapter 4: Construction of Training Datasets for Chemical Reactivity Prediction Through Computational Means
4.1 Introduction
4.2 Oracle Design
4.3 Sampling the Search Space
4.4 Active Learning Strategies
4.5 Automation Software for Accelerated Oracle Design
4.6 Summary and Conclusion
References
Chapter 5: Machine Learned Force Fields: Fundamentals, Their Reach, and Challenges
5.1 Introduction
5.2 Fundamentals of Machine Learning
5.3 Introduction to Neural Networks
5.4 Introduction to Kernel Methods
5.5 Machine Learning in Chemical Reactions and Catalysis
5.6 Overview and Trends in MLFFs
5.7 Neural Network-based Force Fields: The SchNet Case
5.8 Kernel-based Force Fields: The GDML Framework
5.9 Summary and Concluding Remarks
Acknowledgments
References
Part III: Catalyst Optimization and Discovery with Machine Learning
Chapter 6: Optimization of Catalysts Using Computational Chemistry, Machine Learning, and Cheminformatics
6.1 Introduction
6.2 Molecular Descriptors
6.3 Databases
6.4 Cheminformatics
6.5 Automation of QM Protocols
6.6 Automation of ML Protocols
6.7 Concluding Remarks
References
Chapter 7: Predicting Reactivity with Machine Learning
7.1 Introduction
7.2 Yield
7.3 Activation Energy and Rate Constant
7.4 Selectivity
7.5 Turnover Frequency and Volcano Plots
7.6 Summary and Conclusion
References
Chapter 8: Predicting Selectivity in Asymmetric Catalysis with Machine Learning
8.1 Introduction
8.2 Particularities of Enantioselectivity Modeling
8.3 Models for Enantioselectivity
8.4 Summary and Outlook
References
Chapter 9: Artificial Intelligence-assisted Heterogeneous Catalyst Design, Discovery, and Synthesis Utilizing Experimental Data
9.1 Introduction
9.2 Machine Learning Process
9.3 AI-assisted Catalyst Design
9.4 AI-assisted Catalyst Discovery
9.5 AI-assisted Catalyst Synthesis
9.6 Summary and Conclusion
References
Index
End User License Agreement
List of Illustrations
Chapter 1
Figure 1.1 Left: the original dress image that sparked the Internet debate. Rig...
Figure 1.2 Visual representation of the calculation of the tristimulus XYZ values.
Figure 1.3 Top left: a demonstration of the additive properties of overlapping ...
Figure 1.4 A simple demonstration of the distinction between chromaticity (X an...
Figure 1.5 A cone-shaped representation of the HSV color space, showing represe...
Figure 1.6 Top: A visual representation of the CIE–L*a*b* color space. Bottom: ...
Figure 1.7 A simplified top-down view of the inner workings of a DSLR digital c...
Figure 1.8 Top: a visual representation of the RGB array data structure. Bottom...
Figure 1.9 Top: three test tubes filled with three differently colored liquids....
Figure 1.10 Conceptual representation of the contact analysis used for mixing an...
Figure 1.11 Conceptual overview of the construction of the gradient co-occurrenc...
Figure 1.12 Exemplar calculations of GLCM texture metrics for the green highligh...
Figure 1.13 A simplified overview of convolutional operations used for image tra...
Figure 1.14 Exemplar demonstrations of the effect of using the convolutional fil...
Figure 1.15 Use of grayscale profiling and binary image thresholding to characte...
Figure 1.16 A simplified overview of one of two fluorescence amplification casca...
Figure 1.17 (a) Top: a demonstration of how grayscale values on an image can be ...
Figure 1.18 Top: A representation of the photographic hardware and staging used ...
Figure 1.19 An overview of deep neural networks and the layers through which CNN...
Figure 1.20 A visual representation of the IOU evaluation metric is applied to u...
Figure 1.21 From Ref. [25], second (left) and third (right) derivative plots of ...
Figure 1.22 Top: A mechanistic representation of the Miyaura borylation, highlig...
Chapter 2
Figure 2.1 Historic stages in ML-empowered MS. The plot shows the results of th...
Figure 2.2 Applicability of ML depending on the instrument type. (a) Diagram of...
Figure 2.3 Scope of potential applications of ML methods for MS and related ana...
Figure 2.4 Practical considerations for using ML algorithms for MS data process...
Chapter 3
Figure 3.1 Image recognition by ParticlesNN web-service: (a) Image of the Pt/hi...
Figure 3.2 The complexities of TEM image recognition by the neural network (Par...
Figure 3.3 Statistical processing of objects: table generated by LabelMe (Calcu...
Figure 3.4 The DLgram service algorithm: (a) Neural network training followed b...
Figure 3.5 Influence of confidence threshold on the result of recognition: (a) ...
Figure 3.6 Recognition by DLgram of the grains of catalytic carbon porous suppo...
Figure 3.7 Flooding analysis of STM image (Figure 3.1): (a) WSxM; (b) ImageJ.
Figure 3.8 Image recognition by CellPose web service: (a) STM image (Figure 3.1...
Figure 3.9 TEM image of Pt/Al
2
O
3
supported catalysts recognized by ParticlesNN ...
Figure 3.10 TEM image of supported Pd/C catalysts recognized by the DLgram. (a) ...
Figure 3.11 Simultaneous recognition of the nanoparticles and single sites by th...
Figure 3.12 DLgram recognition of the TEM images of [Ir(COD)Cl]
2
-P(Ph)
2
/TiO
2
cat...
Chapter 4
Figure 4.1 Schematic overview of the typical hybrid molecular modeling – ML wor...
Figure 4.2 Schematic overview of the trade-off between compute time and accurac...
Figure 4.3 Benchmarking functional + dispersion correction combinations against...
Figure 4.4 Correlation between the activation energies and the reaction energ ...
Figure 4.5 Reactions were encoded using a differential reaction fingerprint (ra...
Figure 4.6 Flowchart describing the automated workflow implemented. Orange step...
Figure 4.7 A schematic overview of a TS-tools-based TS search. The code makes u...
Chapter 5
Figure 5.1 (a) Machine learning starts with quantum calculations to learn mater...
Figure 5.2 (a) Dataset types: image classification, force field learning, and m...
Figure 5.3 What does Learning mean? (a) Model construction, showcasing the opti...
Figure 5.4 (a) Graphic view of a neuron model. (b) Example of classification of...
Figure 5.5 Examples of activation functions. (a) Threshold (or step) function. ...
Figure 5.6 Some main applications of machine learning in catalysis, from active...
Figure 5.7 (a) SchNet architecture. The atom embeddings are represented in a gr...
Figure 5.8 (a) Atom-type embedding. A schematic representation of the randomly i...
Figure 5.9 Dataset consisting in gradients of a function .
Chapter 6
Figure 6.1 Current state and evolution of computational strategies for catalyst...
Figure 6.2 Definition of cone angle, %V
bur
, and Tolman’s electronic parameter (...
Figure 6.3 Examples of ligands and catalysts from the (a) KRAKEN and (b) OSCAR ...
Figure 6.4 On-demand descriptor database generators: (a) MORFEUS and (b) AQME.
Figure 6.5 Overview of popular cheminformatics tools and applications.
Figure 6.6 General classification of conformational complexity and suggested ty...
Figure 6.7 (a–d) Overview of the protocols automated by the AQME program includ...
Figure 6.8 Overview of tasks automated by ROBERT.
Figure 6.9 Ir-catalyzed activation barriers predicted with ROBERT using a datab...
Chapter 7
Figure 7.1 Overview of the reactivity predictions covered in this work. The rea...
Figure 7.2 Illustration of an energy profile of reactants leading to different p...
Figure 7.3 Architecture of the BERT-based model developed by Schwaller et al. [...
Figure 7.4 The graph convolutional neural network (GCNN) architecture takes as ...
Figure 7.5 (a) Analysis of the reactivity cliff based on the computed interacti...
Figure 7.6 Workflow from Jorner et al. [80] to get the reactant and transition s...
Figure 7.7 (a) Architecture of EQUIREACT, which takes as input the 3D geometrie...
Figure 7.8 Illustration of the comparative molecular field analysis (CoMFA) wor...
Figure 7.9 (a) Architecture of the model developed by Guan et al. [82]. A graph...
Figure 7.10 Volcano plots of (a) cobalt and rhodium catalysts and (b) iridium ca...
Figure 7.11 Illustration of the suggested steps to undergo in order to develop m...
Chapter 8
Figure 8.1 Example of Noyori asymmetric hydrogenation, catalyzed by a Ru cataly...
Figure 8.2 Examples of types of catalysts used in asymmetric catalysis. Note th...
Figure 8.3 Generalized workflow of building and validation of a QSPR model, wit...
Figure 8.4 Examples of training and test set separation techniques for validatio ...
Figure 8.5 The difference between the standard and augmented connectivity matri...
Figure 8.6 Examples of condensed graphs of reactions for asymmetric reactions: ...
Figure 8.7 Prediction of enantioselectivity of IDPi catalysts in asymmetric hyd...
Figure 8.8 ASO descriptors introduced by Zahrt et al. [23]. (a) Calculation of ...
Figure 8.9 The creation of a bag of conformers for the MIL approach. (a) Prepara ...
Chapter 9
Figure 9.1 The traditional catalyst discovery process. Adapted from [14].
Figure 9.2 Surrogate machine learning models developed using experimental data.
Figure 9.3 (a,b) Scheme of robotic-assisted high-throughput catalyst synthesis....
Figure 9.4 (a) Reaction section of a 16-channel high-throughput reactor with he...
Figure 9.5 (a,b) Variation of STY
HA
vs. PAI for two different reaction conditio...
Figure 9.6 Schematic of the active learning process.
Figure 9.7 (a) Plot of the model-predicted and experimental overpotential chang...
Figure 9.8 Contour plots for the variation of predicted Si/Al ratio of FAU vers...
List of Tables
Chapter 1
Table 1.1 Average RGB, HSV, and CIE–L*a*b* pixel values for four 120 × 120 squ...
Table 1.2 Interpretation of the magnitude of color contrast metric.
Chapter 3
Table 3.1 Data sets [21, 22].
Table 3.2 Summary of the quality of the particle count in the test dataset [21...
Table 3.3 Summary of the quality of the neural networks training for STM model...
Table 3.4 Mean particle size estimated by different methods.
Table 3.5 Simultaneous recognition of the nanoparticles and single sites by DL...
Guide
Cover
Table of Contents
Title Page
Copyright
Preface: Shaping the Future of Catalysis Research with Artificial Intelligence
Begin Reading
Index
End User License Agreement
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Artificial Intelligence in Catalysis
Experimental and Computational Methodologies
Edited by
Valentine P. Ananikov
Mikhail V. Polynski
Editors
Prof. Dr. Valentine P. Ananikov
Russian Academy of Sciences
47 Leninsky Prospekt
RS, 119991 Moscow
Russia
Dr. Mikhail V. Polynski
National University of Singapore
4 Engineering Drive
SN, 117585 Singapore
Singapore
Cover Design: Wiley
Cover Image: Courtesy of Evgeny G. Gordeev
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Preface: Shaping the Future of Catalysis Research with Artificial Intelligence
Valentine P. Ananikov and Mikhail V. Polynski
Catalysis has long been at the heart of chemical science, serving as a vital tool for advancing innovations across a wide array of disciplines, from sustainable energy to pharmaceuticals and materials science. As one of the most impactful areas of research, catalysis has shaped modern industry and played a pivotal role in addressing global challenges. However, the increasing complexity of catalytic systems, coupled with the demand for precise, cost-effective solutions, has pushed the boundaries of traditional methodologies. We live in a world undergoing profound, yet still nascent, changes driven by artificial intelligence (AI). While the everyday presence of AI-based tools has become increasingly apparent, these technologies seem to remain in their infancy relative to their transformative potential.
This convergence of catalysis and AI marks the beginning of a paradigm shift. AI offers unprecedented opportunities to accelerate discovery, enhance understanding, and optimize performance in catalysis by bridging computational techniques, data analytics, and chemical intuition. The ability to integrate advanced computational models with experimental data empowers scientists to address long-standing questions in catalysis with newfound precision and scope. As we advance further into the 21st century, the pace of technological innovation continues to accelerate, offering researchers capabilities that were once unimaginable.
The motivation for this book stems from the growing recognition that traditional methods in catalysis research, while immensely valuable, are often constrained by their reliance on time-intensive experimentation and limited predictive power. By consolidating emerging AI-driven approaches, we aim to provide a comprehensive perspective on how AI is reshaping the landscape of catalysis research. It highlights both the opportunities and challenges that lie ahead, showcasing key areas—such as sustainable energy solutions and fine organic synthesis—where the ability to design catalysts for highly selective and efficient reactions will play a pivotal role in addressing global challenges.
This book provides a snapshot of the current research landscape where AI—oftentimes, machine learning—is playing an essential role. The scope of this book is intentionally broad, covering key aspects of catalysis research where AI has made a significant impact. It begins with foundational topics such as computer vision in chemical reaction monitoring and mass spectrometry analysis, emphasizing how AI enhances experimental precision and insight (Chapters 1 and 2). The discussion progresses into specialized applications, including the use of artificial neural networks for microscopy data analysis (Chapter 3) and the construction of high-quality training datasets for chemical reactivity prediction (Chapter 4). These foundational chapters set the stage for the subsequent exploration of predictive and optimization techniques.
The following chapters of the book discuss advanced AI-driven tools, such as machine learning force fields and computational chemistry approaches for catalyst optimization (Chapters 5 and 6). It also examines predictive modeling in reactivity and selectivity, showcasing AI’s ability to forecast outcomes in complex catalytic environments (Chapters 7 and 8). The final chapter brings together the theoretical and practical insights discussed throughout the book, focusing on AI-assisted design, discovery, and synthesis of heterogeneous catalysts using experimental data (Chapter 9).
While the notion of technological singularity remains speculative, rapid advancements suggest that the future may be closer than we anticipate. Autonomous laboratories capable of high-throughput experimentation and more interpretable AI models promise to reshape catalysis in ways once deemed unimaginable. This is especially true for endeavors in sustainable energy, green chemistry, and environmental remediation, where highly selective and efficient catalysts are poised to make a profound impact on global challenges such as climate change and resource scarcity. Moreover, the ethical application of AI in chemistry will open dialogues about transparency, reproducibility, and collaboration, fostering a more inclusive and impactful scientific community. You have likely noticed that we tend to view the future with optimism, and we hope you find these emerging patterns as fascinating as we do.
In closing, we wish to express our gratitude to those whose contributions made this book possible. Mikhail Polynski thanks his wife for her unwavering support through challenging times and for making good moments even better. Both editors extend their deep appreciation to the teams at Wiley-VCH GmbH whose guidance and expertise were invaluable throughout the development of this book.
Lastly, we want to express our deepest gratitude to all the contributors. It has truly been a privilege to collaborate with such a diverse group of scholars—seasoned researchers whose experience is invaluable, as well as younger, yet already accomplished, scientists who represent the future of this field. This balance is particularly valuable to us, as the deep insights of experienced contributors and the fresh perspectives of emerging voices—including yours, our reader—are equally essential in shaping the future of catalysis research.
June 2025 Valentine P. Ananikov
Mikhail V. Polynski
