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AI-Guided Design and Property Prediction for Zeolites and Nanoporous Materials A cohesive and insightful compilation of resources explaining the latest discoveries and methods in the field of nanoporous materials In Artificial Intelligence for Zeolites and Nanoporous Materials: Design, Synthesis and Properties Prediction a team of distinguished researchers delivers a robust compilation of the latest knowledge and most recent developments in computational chemistry, synthetic chemistry, and artificial intelligence as it applies to zeolites, porous molecular materials, covalent organic…mehr
AI-Guided Design and Property Prediction for Zeolites and Nanoporous Materials A cohesive and insightful compilation of resources explaining the latest discoveries and methods in the field of nanoporous materials In Artificial Intelligence for Zeolites and Nanoporous Materials: Design, Synthesis and Properties Prediction a team of distinguished researchers delivers a robust compilation of the latest knowledge and most recent developments in computational chemistry, synthetic chemistry, and artificial intelligence as it applies to zeolites, porous molecular materials, covalent organic frameworks and metal-organic frameworks. The book presents a common language that unifies these fields of research and advances the discovery of new nanoporous materials. The editors have included resources that describe strategies to synthesize new nanoporous materials, construct databases of materials, structure directing agents, and synthesis conditions, and explain computational methods to generate new materials. They also offer material that discusses AI and machine learning algorithms, as well as other, similar approaches to the field. Readers will also find a comprehensive approach to artificial intelligence applied to and written in the language of materials chemistry, guiding the reader through the fundamental questions on how far computer algorithms and numerical representations can drive our search of new nanoporous materials for specific applications. Designed for academic researchers and industry professionals with an interest in synthetic nanoporous materials chemistry, Artificial Intelligence for Zeolites and Nanoporous Materials: Design, Synthesis and Properties Prediction will also earn a place in the libraries of professionals working in large energy, chemical, and biochemical companies with responsibilities related to the design of new nanoporous materials.
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Autorenporträt
German Sastre, PhD, is a member of the Structure Commission of the International Zeolite Association. His research focus is on solid state computational chemistry as applied to nanoporous materials, including zeolites and metal-organic frameworks. Frits Daeyaert, PhD, has a background in computational drug design in the pharmaceutical industry. As visiting scientist at Rice University he has developed and applied de novo design methods for the design of organic structure directing agents for zeolite synthesis. He is a co-recipient of the 2019 Donald W. Breck award in Molecular Sieve Science for his contribution to the discovery of enantiomerically enriched STW zeolite.
Inhaltsangabe
List of Contributors xiii
Preface xvii
About the Cover xxiii
Acknowledgments xxv
1 The Confluence of Organo-Cations, Inorganic Species, and Molecular Modeling on the Discovery of New Zeolite Structures and Compositions 1 Christopher M. Lew, Dan Xie, Joel E. Schmidt, Saleh Elomari, Tracy M. Davis, and Stacey I. Zones
1.1 Introduction 1
1.2 Inorganic Studies 3
1.3 Organic Structure-Directing Agents (OSDAs) 9
1.3.1 Purpose and Important Properties 9
1.3.2 Classes of Ammonium-based OSDAs 10
1.3.3 Methods of Making 12
1.4 OSDA-Zeolite Energetics and Rational Synthesis 15
1.5 Role of High Throughput and Automation 22
1.6 Cataloguing, Archiving, Harvesting, and Mining Years of Historical Data 24
1.7 Concluding Remarks 25
References 25
2 De Novo Design of Organic Structure Directing Agents for the Synthesis of Zeolites 33 Frits Daeyaert and Michael Deem
2.1 Introduction 33
2.2 De Novo Design 34
2.2.1 Molecular Structure Generator 35
2.2.2 Scoring Function 36
2.2.3 Optimization Algorithm 37
2.2.4 Practical Implementation 42
2.3 Scoring Functions for OSDAs 43
2.3.1 Stabilization Energy 43
2.3.2 Other Constraints 44
2.3.3 Multiple Objectives 45
2.4 Applications 48
2.4.1 From Drug Design to the Design of OSDAs for Zeolites 48
2.4.2 Experimental Confirmation: Pure Silica STW 49
2.4.3 Experimental Confirmation: Zeolite AEI 49
2.4.4 Practical Application: SSZ-52 (SFW) 49
2.4.5 Design of Chiral OSDAs to Direct the Synthesis of Chiral STW 49
2.4.6 Design of Selective OSDAs Directed Toward BEA vs. BEB 51
2.4.7 Design of OSDAs for Chiral Zeolite BEA 52
2.4.8 Application of a Machine-Learning Scoring Function in the De Novo Design of OSDAs for Zeolite Beta 52
2.4.9 Design of OSDAs for Zeolites for Gas Adsorption and Separation 52
2.4.9.3 Separation of Ethylene-Ethane: DFT, ACO, NAT, JRY 53
2.4.10 Design of MOFs for Methane Storage and Delivery 54
2.4.11 Multi-Objective De Novo Design of OSDAs for Zeolites Using an Ant Colony Optimization Algorithm 55
2.5 Conclusions and Outlook 55
References 56
3 Machine Learning Search for Suitable Structure Directing Agents for the Synthesis of Beta (BEA) Zeolite Using Molecular Topology and Monte Carlo Techniques 61 María Gálvez-Llompart and German Sastre
3.1 Introduction 61
3.2 Artificial Neural Networks for Modeling Zeolite-SDA van der Waals Energy Applied to BEA Zeolite 64
3.3 Virtual Screening: Identifying Novel SDA with Favorable E ZEO-SDA for the Synthesis of BEA Zeolite 69
3.4 Zeo-SDA Energy Calculation Using Atomic Models 71
3.5 Comparing Zeo-SDA Energy Calculation Using MLR, ANN, and Atomic Models 73
3.6 Conclusions 74
Acknowledgments 77
References 77
4 Generating, Managing, and Mining Big Data in Zeolite Simulations 81 Daniel Schwalbe-Koda and Rafael Gómez-Bombarelli
4.1 Introduction 81
4.1.1 Computational Materials Databases 82
4.1.2 Zeolite Databases 83
4.2 Database of OSDAs for Zeolites 85
4.2.1 Developing a Docking Algorithm 86
4.2.2 Calibrating Binding Energy Predictions 88
4.2.3 Performing and Analyzing High-Throughput Screening Calculations 91
1 The Confluence of Organo-Cations, Inorganic Species, and Molecular Modeling on the Discovery of New Zeolite Structures and Compositions 1 Christopher M. Lew, Dan Xie, Joel E. Schmidt, Saleh Elomari, Tracy M. Davis, and Stacey I. Zones
1.1 Introduction 1
1.2 Inorganic Studies 3
1.3 Organic Structure-Directing Agents (OSDAs) 9
1.3.1 Purpose and Important Properties 9
1.3.2 Classes of Ammonium-based OSDAs 10
1.3.3 Methods of Making 12
1.4 OSDA-Zeolite Energetics and Rational Synthesis 15
1.5 Role of High Throughput and Automation 22
1.6 Cataloguing, Archiving, Harvesting, and Mining Years of Historical Data 24
1.7 Concluding Remarks 25
References 25
2 De Novo Design of Organic Structure Directing Agents for the Synthesis of Zeolites 33 Frits Daeyaert and Michael Deem
2.1 Introduction 33
2.2 De Novo Design 34
2.2.1 Molecular Structure Generator 35
2.2.2 Scoring Function 36
2.2.3 Optimization Algorithm 37
2.2.4 Practical Implementation 42
2.3 Scoring Functions for OSDAs 43
2.3.1 Stabilization Energy 43
2.3.2 Other Constraints 44
2.3.3 Multiple Objectives 45
2.4 Applications 48
2.4.1 From Drug Design to the Design of OSDAs for Zeolites 48
2.4.2 Experimental Confirmation: Pure Silica STW 49
2.4.3 Experimental Confirmation: Zeolite AEI 49
2.4.4 Practical Application: SSZ-52 (SFW) 49
2.4.5 Design of Chiral OSDAs to Direct the Synthesis of Chiral STW 49
2.4.6 Design of Selective OSDAs Directed Toward BEA vs. BEB 51
2.4.7 Design of OSDAs for Chiral Zeolite BEA 52
2.4.8 Application of a Machine-Learning Scoring Function in the De Novo Design of OSDAs for Zeolite Beta 52
2.4.9 Design of OSDAs for Zeolites for Gas Adsorption and Separation 52
2.4.9.3 Separation of Ethylene-Ethane: DFT, ACO, NAT, JRY 53
2.4.10 Design of MOFs for Methane Storage and Delivery 54
2.4.11 Multi-Objective De Novo Design of OSDAs for Zeolites Using an Ant Colony Optimization Algorithm 55
2.5 Conclusions and Outlook 55
References 56
3 Machine Learning Search for Suitable Structure Directing Agents for the Synthesis of Beta (BEA) Zeolite Using Molecular Topology and Monte Carlo Techniques 61 María Gálvez-Llompart and German Sastre
3.1 Introduction 61
3.2 Artificial Neural Networks for Modeling Zeolite-SDA van der Waals Energy Applied to BEA Zeolite 64
3.3 Virtual Screening: Identifying Novel SDA with Favorable E ZEO-SDA for the Synthesis of BEA Zeolite 69
3.4 Zeo-SDA Energy Calculation Using Atomic Models 71
3.5 Comparing Zeo-SDA Energy Calculation Using MLR, ANN, and Atomic Models 73
3.6 Conclusions 74
Acknowledgments 77
References 77
4 Generating, Managing, and Mining Big Data in Zeolite Simulations 81 Daniel Schwalbe-Koda and Rafael Gómez-Bombarelli
4.1 Introduction 81
4.1.1 Computational Materials Databases 82
4.1.2 Zeolite Databases 83
4.2 Database of OSDAs for Zeolites 85
4.2.1 Developing a Docking Algorithm 86
4.2.2 Calibrating Binding Energy Predictions 88
4.2.3 Performing and Analyzing High-Throughput Screening Calculations 91
4.2.4 Rec
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