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Ananikov / Polynski

Artificial Intelligence in Catalysis

Experimental and Computational Methodologies

Medium: Buch
ISBN: 978-3-527-35385-9
Verlag: Wiley-VCH GmbH
Erscheinungstermin: 16.07.2025
vorbestellbar, Erscheinungstermin ca. Juli 2025

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.


Produkteigenschaften


  • Artikelnummer: 9783527353859
  • Medium: Buch
  • ISBN: 978-3-527-35385-9
  • Verlag: Wiley-VCH GmbH
  • Erscheinungstermin: 16.07.2025
  • Sprache(n): Englisch
  • Auflage: 1. Auflage 2025
  • Produktform: Gebunden
  • Seiten: 520
  • Format (B x H): 170 x 244 mm
  • Ausgabetyp: Kein, Unbekannt
Autoren/Hrsg.

Herausgeber

PART 1. MACHINE LEARNING APPLICATIONS IN STRUCTURAL ANALYSIS AND REACTION MONITORING
1) Computer Vision in Chemical Reaction Monitoring and Analysis
2) Machine Learning Meets Mass Spectrometry: a Focused Perspective
3) Application of Artificial Neural Networks in Analysis of Microscopy Data
 
PART 2. QUANTUM CHEMICAL METHODS MEET MACHINE LEARNING
4) Construction of Training Datasets for Chemical Reactivity Prediction Through Computational Means
5)Machine Learned Force Fields: Fundamentals, its Reach, and Challenges
 
PART 3. CATALYST OPTIMIZATION AND DISCOVERY WITH MACHINE LEARNING
6) Optimization of Catalysts using Computational Chemistry, Machine Learning, and Cheminformatics
7) Predicting Reactivity with Machine Learning
8) Predicting Selectivity in Asymmetric Catalysis with Machine Learning
9) Artificial Intelligence-assisted Heterogeneous Catalyst Design, Discovery, and Synthesis Utilizing Experimental Data