Artificial Intelligence In Catalysis
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Artificial Intelligence in Catalysis
Author: Valentine P. Ananikov
language: en
Publisher: John Wiley & Sons
Release Date: 2025-10-06
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.
Asymmetric Catalysis
Author: B. Bosnich
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
The rate of advance in areas of science is seldom constant. Usually certain fields effloresce with activity because of the ~ealization that solutions are possible to long standing important problems. So it is now with asymmetric catalysis, a field which promises to change profoundly the strategic thinking of synthetic chemists. As this Report will show, reagents which can induce catalytic enantiocontrol of chemical transformations could represent the ultimate synthetic method. Nearly all synthetic strategies of complex molecules involve steps which require enantiocontrol and, in many cases, a specific catalytic transformation embodying enan tiocontrol has enormous advantages in terms of the rate and economy of the reaction. Because asymmetric catalysis is in a formative stage, workers with different backgrounds have joined the field. This Workshop had representatives with organometallic, organic, structural, kinetic, enzymatic, microbiological and industrial backgrounds. Each had his own perspective and this Report represents a consensus of this group of eleven people. The result is probably as compre hensive and balanced a view of the subject as is possible at present. It is hoped that those who have until now had but a glancing interest in asymmetric catalysis will find this Report a useful indication of its present state. We believe that asymmetric catalysis will have an increasing impact on the development of chemistry and will eventually dominate much of synthetic and industrial chemistry.
Computational Catalysis
Author: Aravind Asthagiri
language: en
Publisher: Royal Society of Chemistry
Release Date: 2024-12-20
First-principles-based modelling of catalysts is a growing field and the past decade has seen the range of applications for it increase. Improvements in computing power and developments in the areas of machine learning have made many exciting advances possible. The new edition of Computational Catalysis provides an update on the contents of the previous edition whilst introducing new chapters on kinetic Monte Carlo, modelling solvent effects, machine learning for catalyst modelling and design, and modelling complex heterogeneous structures. Written to be accessible to anyone with a familiarity with quantum mechanical methods, this book is a valuable resource for both early career researchers and graduate students.