Computer Simulation of Porous Materials Current Approaches and Future Opportunities 1st Edition by Kim Jelfs ISBN 9781788019002 1788019008

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Product details:
ISBN 10: 1788019008
ISBN 13: 9781788019002
Author: Kim Jelfs

Computer Simulation of Porous Materials Current Approaches and Future Opportunities 1st Edition Table of contents:

Chapter 1 Introduction to Computational Modelling of Microporous Materials

1.1 Introducing Porous Materials Modelling

1.2 An Overview of Microporous Material Classes

1.2.1 Zeolites

1.2.2 Metal–Organic Frameworks (MOFs)

1.2.3 Covalent Organic Frameworks (COFs)

1.2.4 Porous Polymer Networks

1.2.5 Porous Molecular Materials

1.3 An Overview of Modelling Approaches

1.3.1 Structural Characterisation

1.3.2 Role of Flexibility in Porous Materials

1.3.3 Molecular Mechanics

1.3.4 Electronic Structure Methods

1.3.5 Molecular Dynamics

1.3.6 Enhanced Sampling

1.3.7 Grand Canonical Monte Carlo Simulations

1.3.8 Machine Learning (ML)

1.4 Summary

References

Chapter 2 Structure Prediction of Porous Materials

2.1 Why Predict Structures of Porous Materials?

2.1.1 Building Blocks

2.1.2 Bottom-up Approaches

2.1.3 Top-down Approaches

2.2 Structure Generation of Crystalline Network Materials

2.2.1 Software

2.2.2 From a Net to a Crystal Structure

2.2.3 Structure Generation of Zeolites

2.3 Structure Generation of Molecular Materials

2.3.1 Software

2.3.2 Solid-state Structure

2.4 Structure Generation of Amorphous Materials

2.4.1 Software

2.5 Conclusions and Future Perspectives

References

Chapter 3 Atomistic Simulations of Mechanical Properties

3.1 Introduction

3.2 Fundamental Mechanical Properties

3.2.1 Complete Elastic Properties

3.2.2 Young’s Modulus

3.2.3 Linear Compressibility

3.2.4 Poisson’s Ratio

3.2.5 Shear Modulus

3.2.6 Averaging Schemes for Elastic Moduli

3.2.7 Anisotropy of Mechanical Properties

3.2.8 Beyond the Elastic Regime

3.3 Simulation Approaches

3.3.1 Static Methods

3.3.2 Dynamic Methods

3.3.3 Abstract Methods

3.4 Applications of Mechanical Properties

3.4.1 Understanding Mechanical Stability or Instability

3.4.2 Mechanical Surprises

3.5 Summary

Abbreviations

References

Chapter 4 Modelling Sorption and Diffusion Behaviour in Porous Solids

4.1 Introduction

4.2 Molecular Simulations of Adsorption Behaviour

4.2.1 Basics of the Grand Canonical Monte Carlo Method

4.2.2 Brief Overview of Classical Force-fields

4.2.3 Atomic Partial Charge Calculation

4.2.4 Enhanced Sampling Monte Carlo Techniques

4.3 Computational Approaches for Characterising the Structural Properties of Porous Solids

4.3.1 Surface Area

4.3.2 Pore Volume and Size Distribution

4.3.3 Pore Connectivity and Analysis of Topological Features

4.4 Classical Molecular Simulations for Adsorption-based Applications

4.4.1 H2 Gas Storage

4.4.2 Natural Gas Storage

4.4.3 Gas Separation

4.4.4 High-throughput Screening Studies

4.4.5 Challenges and Limitations of Using General Force-fields

4.5 Transport Properties of Gas and Flexibility of Porous Structures

4.5.1 Modelling Guest Diffusion Using Molecular Dynamics

4.5.2 Impact of Framework Flexibility on Guest Diffusion

4.5.3 Modelling Gas Permeability in Porous Membranes

4.6 Summary and Outlook

References

Chapter 5 Spectroscopic and Catalytic Properties

5.1 Introduction

5.2 Spectroscopy, Band Gap, and Magnetism

5.2.1 Vibrational Spectroscopy

5.2.2 UV–visible Spectroscopy

5.2.3 Band Gap

5.2.4 Magnetism

5.3 Catalysis

5.3.1 Condensation Reactions

5.3.2 Hydrolysis of Nerve Agents

5.3.3 Dimerisation of Ethylene

5.3.4 Oxidation of Alkanes

5.3.5 Oxidation of Alkenes

5.3.6 Others

5.3.7 Electrocatalysis

5.4 Conclusions and Perspectives

Abbreviations

References

Chapter 6 Machine Learning in Porous Materials

6.1 Introduction

6.2 General Machine Learning Methods

6.2.1 Overview of Common Machine Learning Algorithms

6.2.2 Practical Considerations

6.2.3 Model Evaluation

6.3 Rationale for Machine Learning in Porous Materials

6.3.1 Defining Characteristics of Porous Materials

6.3.2 Applications of ML in Porous Materials

6.4 Applications of ML to Porous Materials

6.4.1 Model Framing

6.4.2 ML-based Prediction in Porous Materials

6.5 Outlook

References

Subject Index

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