Ananikov V. Artificial Intelligence in Catalysis...Methodologies 2025

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Ananikov V. Artificial Intelligence in Catalysis...Methodologies 2025

Textbook in PDF format 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 Machine Learning Applications in Structural Analysis and Reaction Monitoring Computer Vision in Chemical Reaction Monitoring and Analysis Machine Learning Meets Mass Spectrometry: A Focused Perspective Application of Artificial Neural Networks in the Analysis of Microscopy Data Quantum Chemical Methods Meet Machine Learning Construction of Training Datasets for Chemical Reactivity Prediction Through Computational Means Machine Learned Force Fields: Fundamentals, Their Reach, and Challenges Catalyst Optimization and Discovery with Machine Learning Optimization of Catalysts Using Computational Chemistry, Machine Learning, and Cheminformatics Predicting Reactivity with Machine Learning Predicting Selectivity in Asymmetric Catalysis with Machine Learning Artificial Intelligence-assisted Heterogeneous Catalyst Design, Discovery, and Synthesis Utilizing Experimental Data