Valentine P. Ananikov – författare

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 analysisApplication of artificial neural networks in the analysis of electron microscopy dataConstruction of training datasets for chemical reactivity prediction through computational meansCatalyst optimization and discovery using machine learning modelsPredicting selectivity in asymmetric catalysis with machine learningArtificial 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.

Pullarkat and Pak-Hing Leung: Chiral Metal Complex Promoted Asymmetric Hydrophosphinations.- Masato Tanaka: Recent Progress in Transition Metal-Catalyzed Addition Reactions of H-P(O) Compounds with Unsaturated Carbon Linkages.- Christian Bruneau: Group 8 metals-catalyzed O-H bond addition to unsaturated molecules.- Giorgio Abbiati, Egle M.