Chapter 6. Computer Applications in Catalysis Research

  1. Dr. Gadi Rothenberg

Published Online: 3 MAR 2008

DOI: 10.1002/9783527621866.ch6

Catalysis: Concepts and Green Applications

Catalysis: Concepts and Green Applications

How to Cite

Rothenberg, G. (2008) Computer Applications in Catalysis Research, in Catalysis: Concepts and Green Applications, Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, Germany. doi: 10.1002/9783527621866.ch6

Author Information

  1. Van't Hoff Institute for Molecular Sciences, University of Amsterdam, The Netherlands www.science.uva.nl/˜gadi

Publication History

  1. Published Online: 3 MAR 2008
  2. Published Print: 29 JAN 2008

ISBN Information

Print ISBN: 9783527318247

Online ISBN: 9783527621866

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Keywords:

  • catalysis;
  • green chemistry;
  • computer applications;
  • catalysis research;
  • modeling of catalysts;
  • modeling of catalytic cycles;
  • predictive modeling;
  • rational catalyst design;
  • data-mining methods

Summary

This chapter contains sections titled:

  • Computers as Research Tools in Catalysis

  • Modeling of Catalysts and Catalytic Cycles

    • A Short Overview of Modeling Methods

    • Simplified Model Systems versus Real Reactions

    • Modeling Large Catalyst Systems Using Classical Mechanics

    • In-Depth Reaction Modeling Using Quantum Mechanics

  • Predictive Modeling and Rational Catalyst Design

    • Catalysts, Descriptors, and Figures of Merit

    • Three-Dimensional (3D) Descriptors

      • Comparative Molecular Field Analysis (CoMFA)

      • The Ligand Repulsive Energy Method

    • Two-Dimensional (2D) Descriptors

    • Generating Virtual Catalyst Libraries in Space A 248

    • Understanding Catalyst Diversity

    • Virtual Catalyst Screening: Connecting Spaces A, B, and C

    • Predictive Modeling in Heterogeneous Catalysis

    • Predictive Modeling in Biocatalysis

  • An Overview of Data-Mining Methods in Catalysis

    • Principal Components Analysis (PCA)

    • Partial Least-Squares (PLS) Regression

    • Artificial Neural Networks (ANNs)

    • Classification Trees

    • Model Validation: Separating Knowledge from Garbage

      • Cross-Validation and Bootstrapping

      • Mixing the Dependent Variables (γ-Randomizing)

      • Defining the Model Domain

  • Exercises

  • References