Artificial Intelligence in Catalysis: Experimental and Computational Methodologies

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.