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.

Table of Contents:
Preface: Shaping the Future of Catalysis Research with Artificial Intelligence xi Valentine P. Ananikov and Mikhail V. Polynski Part I Machine Learning Applications in Structural Analysis and Reaction Monitoring 1 1 Computer Vision in Chemical Reaction Monitoring and Analysis 3 Marc Reid 1.1 Introduction 3 1.2 Fundamentals of Computer Vision in Chemistry 4 1.2.1 Color Theory 4 1.2.2 Digital Photography Basics 10 1.3 Computer Vision and Machine Learning in Chemistry 19 1.3.1 Single Image Applications 19 1.3.2 Video Analysis Applications 27 1.4 Summary and Conclusion 31 2 Machine Learning Meets Mass Spectrometry: A Focused Perspective 35 Daniil A. Boiko and Valentine P. Ananikov 2.1 Introduction 35 2.2 Mass Spectrometry in the Machine Learning Era 36 2.3 Mass Spectrometry Methods Landscape and Their Potential for Machine Learning Applications 38 2.4 Representative Mass Spectrometry Applications of Machine Learning 41 2.4.1 Sample Preparation 41 2.4.2 Data Acquisition 42 2.4.3 Data Preprocessing 43 2.4.4 Data Analysis 43 2.5 Protocol for Solving General Problems in Mass Spectrometry Using Machine Learning 45 2.5.1 Data Source 45 2.5.2 Spectra Representation 46 2.5.3 Algorithm Development 47 2.5.4 Metric Selection 48 2.6 Summary and Conclusion 48 3 Application of Artificial Neural Networks in the Analysis of Microscopy Data 55 Anna V. Matveev, Anna G. Okunev, and Anna V. Nartova 3.1 Introduction 55 3.2 Deep Machine Learning for Image Analysis 57 3.2.1 STM Image analysis 57 3.2.2 TEM Image Analysis 60 3.2.3 Comparison of Different Neural Networks 62 3.3 iOk Platform for Automatic Image Analysis 62 3.3.1 Web-service ParticlesNN 63 3.3.2 Chat Bot DLgram 65 3.3.3 No Code ML 68 3.3.4 Comparison of iOk Platform Services with Other Products 70 3.4 Analysis of TEM Images of Heterogeneous Catalyst by iOk Platform 71 3.4.1 Automated Analysis of Supported Catalyst TEM Images 71 3.4.2 High-resolution TEM Images 72 3.4.3 Single Site Analysis 74 3.5 Practical Summary 76 3.6 Future Prospects 77 3.7 Acknowledgments 78 Part II Quantum Chemical Methods Meet Machine Learning 81 4 Construction of Training Datasets for Chemical Reactivity Prediction Through Computational Means 83 Thijs Stuyver and Javier Alfonso-Ramos 4.1 Introduction 83 4.2 Oracle Design 84 4.2.1 Compute Time - Accuracy Trade-off 85 4.2.2 Implications of Optimizing for Multiple Criteria Simultaneously 87 4.2.3 Benchmarking 88 4.2.4 Reproducibility 90 4.3 Sampling the Search Space 91 4.4 Active Learning Strategies 93 4.5 Automation Software for Accelerated Oracle Design 94 4.5.1 autodE 94 4.5.2 RMSD-PP-TS 95 4.5.3 TS-tools 97 4.6 Summary and Conclusion 99 5 Machine Learned Force Fields: Fundamentals, Their Reach, and Challenges 105 Carlos A. Vital-José, Román J. Armenta-Rico and Huziel E. Sauceda 5.1 Introduction 105 5.2 Fundamentals of Machine Learning 107 5.3 Introduction to Neural Networks 110 5.3.1 The Perceptron 110 5.3.2 Multilayer Perceptron 112 5.3.3 The Architecture of a Neural Network 112 5.3.4 Optimization Algorithms 113 5.4 Introduction to Kernel Methods 114 5.5 Machine Learning in Chemical Reactions and Catalysis 115 5.5.1 Selectivity Prediction 116 5.5.2 Catalyst Design and Discovery 116 5.5.3 Experimental Condition Optimizations 117 5.5.4 Active Site Determination 117 5.6 Overview and Trends in MLFFs 118 5.6.1 Neural Network-based FF 118 5.6.2 Kernel-based FF 119 5.7 Neural Network-based Force Fields: The SchNet Case 120 5.7.1 Atom-type Embeddings 120 5.7.2 Interaction Blocks 121 5.7.3 The Explicit SchNet Model for T = 2 122 5.8 Kernel-based Force Fields: The GDML Framework 124 5.9 Summary and Concluding Remarks 126 Part III Catalyst Optimization and Discovery with Machine Learning 131 6 Optimization of Catalysts Using Computational Chemistry, Machine Learning, and Cheminformatics 133 David Dalmau and Juan V. Alegre-Requena 6.1 Introduction 133 6.2 Molecular Descriptors 135 6.3 Databases 136 6.4 Cheminformatics 139 6.5 Automation of QM Protocols 141 6.6 Automation of ML Protocols 143 6.7 Concluding Remarks 145 7 Predicting Reactivity with Machine Learning 157 Lauriane Jacot-Descombes and Kjell Jorner 7.1 Introduction 157 7.2 Yield 160 7.3 Activation Energy and Rate Constant 166 7.4 Selectivity 172 7.5 Turnover Frequency and Volcano Plots 179 7.6 Summary and Conclusion 180 8 Predicting Selectivity in Asymmetric Catalysis with Machine Learning 195 Pavel Sidorov 8.1 Introduction 195 8.2 Particularities of Enantioselectivity Modeling 196 8.2.1 Enantioselectivity as a Target Property 196 8.2.2 Enantioselectivity Data 197 8.2.3 Principles of Reaction Modeling 198 8.3 Models for Enantioselectivity 201 8.3.1 Models Using 2D Descriptors 201 8.3.2 Models Using 3D Descriptors 204 8.4 Summary and Outlook 207 9 Artificial Intelligence-assisted Heterogeneous Catalyst Design, Discovery, and Synthesis Utilizing Experimental Data 213 Rasika Jayarathna, Seyed Majid Ghoreishian, Rahat Javaid, Azadeh Mehrani, Thossaporn Onsree, and Jochen Lauterbach 9.1 Introduction 213 9.2 Machine Learning Process 216 9.2.1 Data Generation 216 9.2.2 Machine Learning Model Development 221 9.3 AI-assisted Catalyst Design 222 9.3.1 Design Rule Extraction via Data Analysis 223 9.3.2 Design Rule Extraction via Model Interpretation 224 9.4 AI-assisted Catalyst Discovery 228 9.4.1 Initial Machine Learning Model 229 9.4.2 Search Space Determination 230 9.4.3 Catalyst Recommendation 230 9.4.4 Catalyst Synthesis and Testing 232 9.4.5 Iterative Process 232 9.4.6 Selected Use Cases from the Literature 233 9.5 AI-assisted Catalyst Synthesis 235 9.6 Summary and Conclusion 238 References 239 Index 249