Computer Simulation of Porous Materials: Current Approaches and Future Opportunities

Cover
Series Preface
Preface
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
			1.2.5.1 Porous Organic Cages
			1.2.5.2 Metal-Organic Polyhedra
	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
			2.1.3.1 What is a Net?
			2.1.3.2 Deriving Nets for Chemistry
			2.1.3.3 Sources of Nets for Chemistry
	2.2 Structure Generation of Crystalline Network Materials
		2.2.1 Software
		2.2.2 From a Net to a Crystal Structure
			2.2.2.1 Choice of Net
			2.2.2.2 Building Blocks and Embedding
			2.2.2.3 Isomerisation
		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
			3.4.2.1 Auxeticity
			3.4.2.2 Negative Linear Compression
			3.4.2.3 Negative Thermal Expansion
	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
			4.2.4.1 Cavity and Energy Bias Methods
			4.2.4.2 Configurational-bias Method
			4.2.4.3 Continuous Fractional-component Monte Carlo Method
	4.3 Computational Approaches for Characterising the Structural Properties of Porous Solids
		4.3.1 Surface Area
			4.3.1.1 Applicability of the BET Method
			4.3.1.2 Surface Areas Derived from Geometric Algorithms
		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
			4.4.5.1 Improving the Performance of General Force-fields
			4.4.5.2 Periodic Models
			4.4.5.3 Polarisable Force-fields
	4.5 Transport Properties of Gas and Flexibility of Porous Structures
		4.5.1 Modelling Guest Diffusion Using Molecular Dynamics
			4.5.1.1 Transport Properties of Multi-component Mixtures
			4.5.1.2 Modelling Rare Events with Transition State Theory
		4.5.2 Impact of Framework Flexibility on Guest Diffusion
		4.5.3 Modelling Gas Permeability in Porous Membranes
			4.5.3.1 Screening Membranes for Separation
			4.5.3.2 Infinite Dilution Calculations
			4.5.3.3 Modelling the Separation Performance of Mixed Matrix Membranes
			4.5.3.3 Modelling the Separation Performance of Mixed Matrix 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
			6.2.1.1 Linear Models
			6.2.1.2 Kernel Models
			6.2.1.3 Bayesian Models
			6.2.1.4 Tree Models and Ensemble Methods
			6.2.1.5 Neural Networks
		6.2.2 Practical Considerations
			6.2.2.1 How to Choose an ML Algorithm
			6.2.2.2 Data Considerations
			6.2.2.3 Model Considerations
		6.2.3 Model Evaluation
			6.2.3.1 Classification Model Evaluation
			6.2.3.2 Regression 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
			6.4.1.1 Data for Porous Materials Development
			6.4.1.2 Descriptors
		6.4.2 ML-based Prediction in Porous Materials
			6.4.2.1 Examples of Property Prediction
			6.4.2.2 Inverse Design
			6.4.2.3 Predicting the Synthesis of Porous Materials
				6.4.2.3.1 Porous Material Synthesis Examples
	6.5 Outlook
	References
Subject Index